E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Topical Collection "Sensors for Globalized Healthy Living and Wellbeing"

Quicklinks

A topical collection in Sensors (ISSN 1424-8220).

Editor

Collection Editor
Prof. Dr. Panicos Kyriacou (Website1, Website2)

School of Mathematics, Computer Science and Engineering, City University London, Northampton Square, London, EC1V 0HB, UK
Interests: biomedical optical sensors; tissue optics; spectrophotometry; bio-instrumentation; physiological measurement

Topical Collection Information

Dear Colleagues,

Almost every decision relating to prognosis, diagnosis, treatment and routine clinical monitoring of patients cannot be done without the assistance of medical technologies. As the capabilities of sensing technologies increased, so has the interest of researchers, clinicians and policy-makers in its potential. Recording of physiological and psychological variables in real-life conditions could be especially useful in management of chronic disorders or other health challenges e.g. for high blood pressure, diabetes, anorexia nervosa, chronic pain or severe obesity, stress, epilepsy, depression and many others. Public attitudes to technology and wellbeing have evolved and there is great interest amongst the general public in personalised healthcare. Such attitudes have inspired the development of intelligent sensor technologies, predominantly those for the non-invasive monitoring of various physiological parameters in homes, businesses, and health clubs. Real-life long-term monitoring of health could be useful for measurement of treatment effects at home, in a situation where subjects feel most comfortable. Also, increasing life expectancy accompanied with decreasing dependency ratio in developed countries calls for new solutions to support independent living of the elderly and other vulnerable groups. Wearable sensor technology may provide an integral part of the solution for providing health care to a growing world population that will be strained by a ballooning aging population. Potential applications of these proposed technologies, could include the early diagnosis of diseases such as congestive heart failure, the prevention and/or management of chronic conditions such as diabetes, improved clinical management of neurodegenerative conditions such as Parkinson's disease, and the ability to promptly respond to emergency situations such as seizures in patients with epilepsy and cardiac arrest in subjects undergoing cardiovascular monitoring. In addition, employing wearable technology in professions where people are exposed to extreme environments, dangers or hazards could help save their lives and protect health-care personnel.

This topical collection invites submissions in this area, particularly those that are application-focused.

Prof. Dr. Panicos Kyriacou
Collection Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Papers will be published continuously (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).


Published Papers (64 papers)

2016

Jump to: 2015, 2014, 2013

Open AccessReview Sensor Fusion and Smart Sensor in Sports and Biomedical Applications
Sensors 2016, 16(10), 1569; doi:10.3390/s16101569
Received: 28 June 2016 / Revised: 1 September 2016 / Accepted: 13 September 2016 / Published: 23 September 2016
PDF Full-text (4274 KB) | HTML Full-text | XML Full-text
Abstract
The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some [...] Read more.
The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others. Full article
Figures

Figure 1

Open AccessArticle Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network
Sensors 2016, 16(10), 1566; doi:10.3390/s16101566
Received: 13 June 2016 / Revised: 8 September 2016 / Accepted: 20 September 2016 / Published: 22 September 2016
PDF Full-text (2145 KB) | HTML Full-text | XML Full-text
Abstract
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to [...] Read more.
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. Full article
Figures

Figure 1

Open AccessArticle Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction
Sensors 2016, 16(9), 1431; doi:10.3390/s16091431
Received: 23 July 2016 / Revised: 24 August 2016 / Accepted: 30 August 2016 / Published: 6 September 2016
PDF Full-text (26027 KB) | HTML Full-text | XML Full-text
Abstract
Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine [...] Read more.
Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. Full article
Figures

Open AccessReview Techniques for Interface Stress Measurements within Prosthetic Sockets of Transtibial Amputees: A Review of the Past 50 Years of Research
Sensors 2016, 16(7), 1119; doi:10.3390/s16071119
Received: 18 March 2016 / Revised: 12 May 2016 / Accepted: 2 June 2016 / Published: 20 July 2016
PDF Full-text (4099 KB) | HTML Full-text | XML Full-text
Abstract
The distribution of interface stresses between the residual limb and prosthetic socket of a transtibial amputee has been considered as a direct indicator of the socket quality fit and comfort. Therefore, researchers have been very interested in quantifying these interface stresses in [...] Read more.
The distribution of interface stresses between the residual limb and prosthetic socket of a transtibial amputee has been considered as a direct indicator of the socket quality fit and comfort. Therefore, researchers have been very interested in quantifying these interface stresses in order to evaluate the extent of any potential damage caused by the socket to the residual limb tissues. During the past 50 years a variety of measurement techniques have been employed in an effort to identify sites of excessive stresses which may lead to skin breakdown, compare stress distributions in various socket designs, and evaluate interface cushioning and suspension systems, among others. The outcomes of such measurement techniques have contributed to improving the design and fitting of transtibial sockets. This article aims to review the operating principles, advantages, and disadvantages of conventional and emerging techniques used for interface stress measurements inside transtibial sockets. It also reviews and discusses the evolution of different socket concepts and interface stress investigations conducted in the past five decades, providing valuable insights into the latest trends in socket designs and the crucial considerations for effective stress measurement tools that lead to a functional prosthetic socket. Full article
Open AccessArticle Stair-Walking Performance in Adolescents with Intellectual Disabilities
Sensors 2016, 16(7), 1066; doi:10.3390/s16071066
Received: 21 March 2016 / Revised: 1 July 2016 / Accepted: 8 July 2016 / Published: 11 July 2016
PDF Full-text (3937 KB) | HTML Full-text | XML Full-text
Abstract
Most individuals with intellectual disabilities (ID) demonstrate problems in learning and movement coordination. Consequently, they usually have difficulties in activities such as standing, walking, and stair climbing. To monitor the physical impairments of these children, regular gross motor evaluation is crucial. Straight-line [...] Read more.
Most individuals with intellectual disabilities (ID) demonstrate problems in learning and movement coordination. Consequently, they usually have difficulties in activities such as standing, walking, and stair climbing. To monitor the physical impairments of these children, regular gross motor evaluation is crucial. Straight-line level walking is the most frequently used test of their mobility. However, numerous studies have found that unless the children have multiple disabilities, no significant differences can be found between the children with ID and typically-developed children in this test. Stair climbing presents more challenges than level walking because it is associated with numerous physical factors, including lower extremity strength, cardiopulmonary endurance, vision, balance, and fear of falling. Limited ability in those factors is one of the most vital markers for children with ID. In this paper, we propose a sensor-based approach for measuring stair-walking performance, both upstairs and downstairs, for adolescents with ID. Particularly, we address the problem of sensor calibration to ensure measurement accuracy. In total, 62 participants aged 15 to 21 years, namely 32 typically-developed (TD) adolescents, 20 adolescents with ID, and 10 adolescents with multiple disabilities (MD), participated. The experimental results showed that stair-walking is more sensitive than straight-line level walking in capturing gait characteristics for adolescents with ID. Full article
Open AccessArticle On Curating Multimodal Sensory Data for Health and Wellness Platforms
Sensors 2016, 16(7), 980; doi:10.3390/s16070980
Received: 4 February 2016 / Revised: 14 June 2016 / Accepted: 21 June 2016 / Published: 27 June 2016
Cited by 1 | PDF Full-text (6307 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of [...] Read more.
In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data. Full article
Open AccessArticle Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson’s Disease
Sensors 2016, 16(6), 930; doi:10.3390/s16060930
Received: 30 March 2016 / Revised: 4 June 2016 / Accepted: 16 June 2016 / Published: 21 June 2016
PDF Full-text (3346 KB) | HTML Full-text | XML Full-text
Abstract
Remote monitoring of Parkinson’s Disease (PD) patients with inertia sensors is a relevant method for a better assessment of symptoms. We present a new approach for symptom quantification based on motion data: the automatic Unified Parkinson Disease Rating Scale (UPDRS) classification in [...] Read more.
Remote monitoring of Parkinson’s Disease (PD) patients with inertia sensors is a relevant method for a better assessment of symptoms. We present a new approach for symptom quantification based on motion data: the automatic Unified Parkinson Disease Rating Scale (UPDRS) classification in combination with an animated 3D avatar giving the neurologist the impression of having the patient live in front of him. In this study we compared the UPDRS ratings of the pronation-supination task derived from: (a) an examination based on video recordings as a clinical reference; (b) an automatically classified UPDRS; and (c) a UPDRS rating from the assessment of the animated 3D avatar. Data were recorded using Magnetic, Angular Rate, Gravity (MARG) sensors with 15 subjects performing a pronation-supination movement of the hand. After preprocessing, the data were classified with a J48 classifier and animated as a 3D avatar. Video recording of the movements, as well as the 3D avatar, were examined by movement disorder specialists and rated by UPDRS. The mean agreement between the ratings based on video and (b) the automatically classified UPDRS is 0.48 and with (c) the 3D avatar it is 0.47. The 3D avatar is similarly suitable for assessing the UPDRS as video recordings for the examined task and will be further developed by the research team. Full article
Figures

Open AccessReview A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
Sensors 2016, 16(6), 831; doi:10.3390/s16060831
Received: 12 April 2016 / Revised: 23 May 2016 / Accepted: 2 June 2016 / Published: 7 June 2016
Cited by 1 | PDF Full-text (871 KB) | HTML Full-text | XML Full-text
Abstract
Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor [...] Read more.
Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments. Full article
Figures

Open AccessArticle A Wireless Pressure Sensor Integrated with a Biodegradable Polymer Stent for Biomedical Applications
Sensors 2016, 16(6), 809; doi:10.3390/s16060809
Received: 31 March 2016 / Revised: 23 May 2016 / Accepted: 26 May 2016 / Published: 2 June 2016
PDF Full-text (3832 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes the fabrication and characterization of a wireless pressure sensor for smart stent applications. The micromachined pressure sensor has an area of 3.13 × 3.16 mm2 and is fabricated with a photosensitive SU-8 polymer. The wireless pressure sensor comprises [...] Read more.
This paper describes the fabrication and characterization of a wireless pressure sensor for smart stent applications. The micromachined pressure sensor has an area of 3.13 × 3.16 mm2 and is fabricated with a photosensitive SU-8 polymer. The wireless pressure sensor comprises a resonant circuit and can be used without the use of an internal power source. The capacitance variations caused by changes in the intravascular pressure shift the resonance frequency of the sensor. This change can be detected using an external antenna, thus enabling the measurement of the pressure changes inside a tube with a simple external circuit. The wireless pressure sensor is capable of measuring pressure from 0 mmHg to 230 mmHg, with a sensitivity of 0.043 MHz/mmHg. The biocompatibility of the pressure sensor was evaluated using cardiac cells isolated from neonatal rat ventricular myocytes. After inserting a metal stent integrated with the pressure sensor into a cardiovascular vessel of an animal, medical systems such as X-ray were employed to consistently monitor the condition of the blood vessel. No abnormality was found in the animal blood vessel for approximately one month. Furthermore, a biodegradable polymer (polycaprolactone) stent was fabricated with a 3D printer. The polymer stent exhibits better sensitivity degradation of the pressure sensor compared to the metal stent. Full article
Open AccessArticle A Dual-Field Sensing Scheme for a Guidance System for the Blind
Sensors 2016, 16(5), 667; doi:10.3390/s16050667
Received: 14 March 2016 / Revised: 24 April 2016 / Accepted: 28 April 2016 / Published: 11 May 2016
PDF Full-text (9659 KB) | HTML Full-text | XML Full-text
Abstract
An electronic guidance system is very helpful in improving blind people’s perceptions in a local environment. In our previous work “Lin, Q.; Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile Model. Sensors 2014, 14, 18670–18700”, a [...] Read more.
An electronic guidance system is very helpful in improving blind people’s perceptions in a local environment. In our previous work “Lin, Q.; Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile Model. Sensors 2014, 14, 18670–18700”, a context-aware guidance system using a combination of a laser scanner and a camera was proposed. By using a near-field graphical model, the proposed system could interpret a near-field scene in very high resolution. In this paper, our work is extended by adding a far-field graphical model. The integration of the near-field and the far-field models constitutes a dual-field sensing scheme. In the near-field range, reliable inference of the ground and object status is obtained by fusing range data and image data using the near-field graphical model. In the far-field range, which only the camera can cover, the far-field graphical model is proposed to interpret far-field image data based on appearance and spatial prototypes built using the near-field interpreted data. The dual-field sensing scheme provides a solution for the guidance systems to optimise their scene interpretation capability using simple sensor configurations. Experiments under various local conditions were conducted to show the efficiency of the proposed scheme in improving blind people’s perceptions in urban environments. Full article
Open AccessArticle Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors
Sensors 2016, 16(3), 409; doi:10.3390/s16030409
Received: 29 December 2015 / Revised: 26 February 2016 / Accepted: 15 March 2016 / Published: 19 March 2016
PDF Full-text (5457 KB) | HTML Full-text | XML Full-text
Abstract
Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the [...] Read more.
Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the need for physical confinement. In this study, we describe a physiological signal monitoring bed based on load cells and assess an algorithm to extract the heart rate and breathing rate from the measured load-cell signals. Four infants participated in a total of 13 experiments. As a reference signal, electrocardiogram and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiration information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiration information were found to have average performance errors of 2.55% and 2.66%, respectively. The experimental results verify the positive feasibility of BCG-based measurements in infants. Full article

2015

Jump to: 2016, 2014, 2013

Open AccessArticle Tilted Orientation of Photochromic Dyes with Guest-Host Effect of Liquid Crystalline Polymer Matrix for Electrical UV Sensing
Sensors 2016, 16(1), 38; doi:10.3390/s16010038
Received: 25 November 2015 / Revised: 17 December 2015 / Accepted: 25 December 2015 / Published: 29 December 2015
PDF Full-text (3872 KB) | HTML Full-text | XML Full-text
Abstract
We propose a highly oriented photochromic dye film for an ultraviolet (UV)-sensing layer, where spirooxazine (SO) derivatives are aligned with the liquid crystalline UV-curable reactive mesogens (RM) using a guest-host effect. For effective electrical UV sensing with a simple metal-insulator-metal structure, our [...] Read more.
We propose a highly oriented photochromic dye film for an ultraviolet (UV)-sensing layer, where spirooxazine (SO) derivatives are aligned with the liquid crystalline UV-curable reactive mesogens (RM) using a guest-host effect. For effective electrical UV sensing with a simple metal-insulator-metal structure, our results show that the UV-induced switchable dipole moment amount of the SO derivatives is high; however, their tilting orientation should be controlled. Compared to the dielectric layer with the nearly planar SO dye orientation, the photochromic dielectric layer with the moderately tilted dye orientation shows more than seven times higher the UV-induced capacitance variation. Full article
Open AccessArticle One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor
Sensors 2015, 15(12), 31999-32019; doi:10.3390/s151229907
Received: 2 October 2015 / Revised: 9 December 2015 / Accepted: 11 December 2015 / Published: 19 December 2015
PDF Full-text (992 KB) | HTML Full-text | XML Full-text
Abstract
A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics [...] Read more.
A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person. Full article
Open AccessArticle Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms
Sensors 2015, 15(11), 29393-29407; doi:10.3390/s151129393
Received: 25 September 2015 / Revised: 4 November 2015 / Accepted: 17 November 2015 / Published: 20 November 2015
PDF Full-text (806 KB) | HTML Full-text | XML Full-text
Abstract
The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict [...] Read more.
The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population. Full article
Open AccessArticle Identification of Foot Pathologies Based on Plantar Pressure Asymmetry
Sensors 2015, 15(8), 20392-20408; doi:10.3390/s150820392
Received: 13 March 2015 / Revised: 7 August 2015 / Accepted: 11 August 2015 / Published: 18 August 2015
Cited by 3 | PDF Full-text (1685 KB) | HTML Full-text | XML Full-text
Abstract
Foot pathologies can negatively influence foot function, consequently impairing gait during daily activity, and severely impacting an individual’s quality of life. These pathologies are often painful and correspond with high or abnormal plantar pressure, which can result in asymmetry in the pressure [...] Read more.
Foot pathologies can negatively influence foot function, consequently impairing gait during daily activity, and severely impacting an individual’s quality of life. These pathologies are often painful and correspond with high or abnormal plantar pressure, which can result in asymmetry in the pressure distribution between the two feet. There is currently no general consensus on the presence of asymmetry in able-bodied gait, and plantar pressure analysis during gait is in dire need of a standardized method to quantify asymmetry. This paper investigates the use of plantar pressure asymmetry for pathological gait diagnosis. The results of this study involving plantar pressure analysis in fifty one participants (31 healthy and 20 with foot pathologies) support the presence of plantar pressure asymmetry in normal gait. A higher level of asymmetry was detected at the majority of the regions in the feet of the pathological population, including statistically significant differences in the plantar pressure asymmetry in two regions of the foot, metatarsophalangeal joint 3 (MPJ3) and the lateral heel. Quantification of plantar pressure asymmetry may prove to be useful for the identification and diagnosis of various foot pathologies. Full article
Open AccessArticle Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform
Sensors 2015, 15(6), 14142-14161; doi:10.3390/s150614142
Received: 17 December 2014 / Accepted: 23 March 2015 / Published: 16 June 2015
PDF Full-text (985 KB) | HTML Full-text | XML Full-text
Abstract
We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged [...] Read more.
We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity. Full article
Open AccessArticle A Wavelet-Based Approach to Fall Detection
Sensors 2015, 15(5), 11575-11586; doi:10.3390/s150511575
Received: 24 February 2015 / Revised: 29 April 2015 / Accepted: 13 May 2015 / Published: 20 May 2015
Cited by 3 | PDF Full-text (810 KB) | HTML Full-text | XML Full-text
Abstract
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel [...] Read more.
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. Full article
Open AccessArticle Heart Rate Variability Monitoring during Sleep Based on Capacitively Coupled Textile Electrodes on a Bed
Sensors 2015, 15(5), 11295-11311; doi:10.3390/s150511295
Received: 17 March 2015 / Accepted: 8 May 2015 / Published: 14 May 2015
Cited by 3 | PDF Full-text (1465 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we developed and tested a capacitively coupled electrocardiogram (ECG) measurement system using conductive textiles on a bed, for long-term healthcare monitoring. The system, which was designed to measure ECG in a bed with no constraints of sleep position and [...] Read more.
In this study, we developed and tested a capacitively coupled electrocardiogram (ECG) measurement system using conductive textiles on a bed, for long-term healthcare monitoring. The system, which was designed to measure ECG in a bed with no constraints of sleep position and posture, included a foam layer to increase the contact region with the curvature of the body and a cover to ensure durability and easy installation. Nine healthy subjects participated in the experiment during polysomnography (PSG), and the heart rate (HR) coverage and heart rate variability (HRV) parameters were analyzed to evaluate the system. The experimental results showed that the mean of R-peak coverage was 98.0% (95.5%–99.7%), and the normalized errors of HRV time and spectral measures between the Ag/AgCl system and our system ranged from 0.15% to 4.20%. The root mean square errors for inter-beat (RR) intervals and HR were 1.36 ms and 0.09 bpm, respectively. We also showed the potential of our developed system for rapid eye movement (REM) sleep and wake detection as well as for recording of abnormal states. Full article
Open AccessReview The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development
Sensors 2015, 15(5), 11312-11362; doi:10.3390/s150511312
Received: 2 March 2015 / Accepted: 8 May 2015 / Published: 14 May 2015
Cited by 5 | PDF Full-text (923 KB) | HTML Full-text | XML Full-text
Abstract
Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications [...] Read more.
Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people’s independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application. Full article
Open AccessArticle Tracking Systems for Virtual Rehabilitation: Objective Performance vs. Subjective Experience. A Practical Scenario
Sensors 2015, 15(3), 6586-6606; doi:10.3390/s150306586
Received: 30 January 2015 / Revised: 5 March 2015 / Accepted: 13 March 2015 / Published: 19 March 2015
Cited by 2 | PDF Full-text (4074 KB) | HTML Full-text | XML Full-text
Abstract
Motion tracking systems are commonly used in virtual reality-based interventions to detect movements in the real world and transfer them to the virtual environment. There are different tracking solutions based on different physical principles, which mainly define their performance parameters. However, special [...] Read more.
Motion tracking systems are commonly used in virtual reality-based interventions to detect movements in the real world and transfer them to the virtual environment. There are different tracking solutions based on different physical principles, which mainly define their performance parameters. However, special requirements have to be considered for rehabilitation purposes. This paper studies and compares the accuracy and jitter of three tracking solutions (optical, electromagnetic, and skeleton tracking) in a practical scenario and analyzes the subjective perceptions of 19 healthy subjects, 22 stroke survivors, and 14 physical therapists. The optical tracking system provided the best accuracy (1.074 ± 0.417 cm) while the electromagnetic device provided the most inaccurate results (11.027 ± 2.364 cm). However, this tracking solution provided the best jitter values (0.324 ± 0.093 cm), in contrast to the skeleton tracking, which had the worst results (1.522 ± 0.858 cm). Healthy individuals and professionals preferred the skeleton tracking solution rather than the optical and electromagnetic solution (in that order). Individuals with stroke chose the optical solution over the other options. Our results show that subjective perceptions and preferences are far from being constant among different populations, thus suggesting that these considerations, together with the performance parameters, should be also taken into account when designing a rehabilitation system. Full article
Open AccessArticle New Lower-Limb Gait Asymmetry Indices Based on a Depth Camera
Sensors 2015, 15(3), 4605-4623; doi:10.3390/s150304605
Received: 23 October 2014 / Revised: 13 January 2015 / Accepted: 9 February 2015 / Published: 24 February 2015
Cited by 7 | PDF Full-text (1113 KB) | HTML Full-text | XML Full-text
Abstract
Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the [...] Read more.
Background: Various asymmetry indices have been proposed to compare the spatiotemporal, kinematic and kinetic parameters of lower limbs during the gait cycle. However, these indices rely on gait measurement systems that are costly and generally require manual examination, calibration procedures and the precise placement of sensors/markers on the body of the patient. Methods: To overcome these issues, this paper proposes a new asymmetry index, which uses an inexpensive, easy-to-use and markerless depth camera (Microsoft Kinect™) output. This asymmetry index directly uses depth images provided by the Kinect™ without requiring joint localization. It is based on the longitudinal spatial difference between lower-limb movements during the gait cycle. To evaluate the relevance of this index, fifteen healthy subjects were tested on a treadmill walking normally and then via an artificially-induced gait asymmetry with a thick sole placed under one shoe. The gait movement was simultaneously recorded using a Kinect™ placed in front of the subject and a motion capture system. Results: The proposed longitudinal index distinguished asymmetrical gait (p < 0.001), while other symmetry indices based on spatiotemporal gait parameters failed using such Kinect™ skeleton measurements. Moreover, the correlation coefficient between this index measured by Kinect™ and the ground truth of this index measured by motion capture is 0.968. Conclusion: This gait asymmetry index measured with a Kinect™ is low cost, easy to use and is a promising development for clinical gait analysis. Full article
Open AccessArticle False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information
Sensors 2015, 15(2), 3952-3974; doi:10.3390/s150203952
Received: 11 December 2014 / Accepted: 30 January 2015 / Published: 9 February 2015
PDF Full-text (1986 KB) | HTML Full-text | XML Full-text
Abstract
False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, [...] Read more.
False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring. Full article
Figures

Open AccessArticle Embroidered Electrode with Silver/Titanium Coating for Long-Term ECG Monitoring
Sensors 2015, 15(1), 1750-1759; doi:10.3390/s150101750
Received: 8 October 2014 / Accepted: 16 December 2014 / Published: 15 January 2015
Cited by 5 | PDF Full-text (1949 KB) | HTML Full-text | XML Full-text
Abstract
For the long-time monitoring of electrocardiograms, electrodes must be skin-friendly and non-irritating, but in addition they must deliver leads without artifacts even if the skin is dry and the body is moving. Today’s adhesive conducting gel electrodes are not suitable for such [...] Read more.
For the long-time monitoring of electrocardiograms, electrodes must be skin-friendly and non-irritating, but in addition they must deliver leads without artifacts even if the skin is dry and the body is moving. Today’s adhesive conducting gel electrodes are not suitable for such applications. We have developed an embroidered textile electrode from polyethylene terephthalate yarn which is plasma-coated with silver for electrical conductivity and with an ultra-thin titanium layer on top for passivation. Two of these electrodes are embedded into a breast belt. They are moisturized with a very low amount of water vapor from an integrated reservoir. The combination of silver, titanium and water vapor results in an excellent electrode chemistry. With this belt the long-time monitoring of electrocardiography (ECG) is possible at rest as well as when the patient is moving. Full article
Open AccessArticle Kinect as a Tool for Gait Analysis: Validation of a Real-Time Joint Extraction Algorithm Working in Side View
Sensors 2015, 15(1), 1417-1434; doi:10.3390/s150101417
Received: 22 September 2014 / Accepted: 7 January 2015 / Published: 14 January 2015
Cited by 7 | PDF Full-text (3500 KB) | HTML Full-text | XML Full-text
Abstract
The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still [...] Read more.
The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still limits the adoption of the device to a number of relevant applications. This paper presents an algorithm to locate and estimate the trajectories of up to six joints extracted from the side depth view of a human body captured by the Kinect device. The algorithm is then applied to extract data that can be exploited to provide an objective score for the “Get Up and Go Test”, which is typically adopted for gait analysis in rehabilitation fields. Starting from the depth-data stream provided by the Microsoft Kinect sensor, the proposed algorithm relies on anthropometric models only, to locate and identify the positions of the joints. Differently from machine learning approaches, this solution avoids complex computations, which usually require significant resources. The reliability of the information about the joint position output by the algorithm is evaluated by comparison to a marker-based system. Tests show that the trajectories extracted by the proposed algorithm adhere to the reference curves better than the ones obtained from the skeleton generated by the native applications provided within the Microsoft Kinect (Microsoft Corporation, Redmond,WA, USA, 2013) and OpenNI (OpenNI organization, Tel Aviv, Israel, 2013) Software Development Kits. Full article

2014

Jump to: 2016, 2015, 2013

Open AccessArticle Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields
Sensors 2015, 15(1), 135-147; doi:10.3390/s150100135
Received: 4 September 2014 / Accepted: 19 December 2014 / Published: 24 December 2014
Cited by 8 | PDF Full-text (753 KB) | HTML Full-text | XML Full-text
Abstract
Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D) space. In this research, we use 3D depth information from hand motions, [...] Read more.
Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D) space. In this research, we use 3D depth information from hand motions, generated from Microsoft’s Kinect sensor and apply a hierarchical conditional random field (CRF) that recognizes hand signs from the hand motions. The proposed method uses a hierarchical CRF to detect candidate segments of signs using hand motions, and then a BoostMap embedding method to verify the hand shapes of the segmented signs. Experiments demonstrated that the proposed method could recognize signs from signed sentence data at a rate of 90.4%. Full article
Open AccessArticle Biosignal PI, an Affordable Open-Source ECG and Respiration Measurement System
Sensors 2015, 15(1), 93-109; doi:10.3390/s150100093
Received: 30 October 2014 / Accepted: 15 December 2014 / Published: 23 December 2014
Cited by 2 | PDF Full-text (627 KB) | HTML Full-text | XML Full-text
Abstract
Bioimedical pilot projects e.g., telemedicine, homecare, animal and human trials usually involve several physiological measurements. Technical development of these projects is time consuming and in particular costly. A versatile but affordable biosignal measurement platform can help to reduce time and risk while [...] Read more.
Bioimedical pilot projects e.g., telemedicine, homecare, animal and human trials usually involve several physiological measurements. Technical development of these projects is time consuming and in particular costly. A versatile but affordable biosignal measurement platform can help to reduce time and risk while keeping the focus on the important goal and making an efficient use of resources. In this work, an affordable and open source platform for development of physiological signals is proposed. As a first step an 8–12 leads electrocardiogram (ECG) and respiration monitoring system is developed. Chips based on iCoupler technology have been used to achieve electrical isolation as required by IEC 60601 for patient safety. The result shows the potential of this platform as a base for prototyping compact, affordable, and medically safe measurement systems. Further work involves both hardware and software development to develop modules. These modules may require development of front-ends for other biosignals or just collect data wirelessly from different devices e.g., blood pressure, weight, bioimpedance spectrum, blood glucose, e.g., through Bluetooth. All design and development documents, files and source codes will be available for non-commercial use through project website, BiosignalPI.org. Full article
Open AccessArticle Assessment of a Newly Developed, Active Pneumatic-Driven, Sensorimotor Test and Training Device
Sensors 2014, 14(12), 24174-24187; doi:10.3390/s141224174
Received: 17 September 2014 / Revised: 20 November 2014 / Accepted: 8 December 2014 / Published: 15 December 2014
PDF Full-text (1299 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The sensorimotor system (SMS) plays an important role in sports and in every day movement. Several tools for assessment and training have been designed. Many of them are directed to specific populations, and have major shortcomings due to the training effect or [...] Read more.
The sensorimotor system (SMS) plays an important role in sports and in every day movement. Several tools for assessment and training have been designed. Many of them are directed to specific populations, and have major shortcomings due to the training effect or safety. The aim of the present study was to design and assess a dynamic sensorimotor test and training device that can be adjusted for all levels of performance. The novel pneumatic-driven mechatronic device can guide the trainee, allow independent movements or disrupt the individual with unpredicted perturbations while standing on a platform. The test-reliability was evaluated using intraclass correlation coefficient (ICC). Subjects were required to balance their center of pressure (COP) in a target circle (TITC). The time in TITC and the COP error (COPe) were recorded for analysis. The results of 22 males and 14 females (23.7 ± 2.6 years) showed good to excellent test–retest reliability. The newly designed Active Balance System (ABS) was then compared with the Biodex Balance System SD® (BBS). The results of 15 females, 14 males (23.4 ± 1.6 years) showed modest correlation in static and acceptable correlation in dynamic conditions, suggesting that ABS could be a reliable and comparable tool for dynamic balance assessments. Full article
Open AccessArticle Piezoelectric Bimorphs’ Characteristics as In-Socket Sensors for Transfemoral Amputees
Sensors 2014, 14(12), 23724-23741; doi:10.3390/s141223724
Received: 12 August 2014 / Revised: 23 October 2014 / Accepted: 5 November 2014 / Published: 10 December 2014
Cited by 3 | PDF Full-text (1243 KB) | HTML Full-text | XML Full-text
Abstract
Alternative sensory systems for the development of prosthetic knees are being increasingly highlighted nowadays, due to the rapid advancements in the field of lower limb prosthetics. This study presents the use of piezoelectric bimorphs as in-socket sensors for transfemoral amputees. An Instron [...] Read more.
Alternative sensory systems for the development of prosthetic knees are being increasingly highlighted nowadays, due to the rapid advancements in the field of lower limb prosthetics. This study presents the use of piezoelectric bimorphs as in-socket sensors for transfemoral amputees. An Instron machine was used in the calibration procedure and the corresponding output data were further analyzed to determine the static and dynamic characteristics of the piezoelectric bimorph. The piezoelectric bimorph showed appropriate static operating range, repeatability, hysteresis, and frequency response for application in lower prosthesis, with a force range of 0–100 N. To further validate this finding, an experiment was conducted with a single transfemoral amputee subject to measure the stump/socket pressure using the piezoelectric bimorph embedded inside the socket. The results showed that a maximum interface pressure of about 27 kPa occurred at the anterior proximal site compared to the anterior distal and posterior sites, consistent with values published in other studies. This paper highlighted the capacity of piezoelectric bimorphs to perform as in-socket sensors for transfemoral amputees. However, further experiments are recommended to be conducted with different amputees with different socket types. Full article
Open AccessArticle Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems
Sensors 2014, 14(12), 23230-23247; doi:10.3390/s141223230
Received: 1 August 2014 / Revised: 18 September 2014 / Accepted: 27 November 2014 / Published: 5 December 2014
Cited by 6 | PDF Full-text (3890 KB) | HTML Full-text | XML Full-text
Abstract
Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. [...] Read more.
Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR) digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases. Full article
Open AccessArticle Long-Term Activity Recognition from Wristwatch Accelerometer Data
Sensors 2014, 14(12), 22500-22524; doi:10.3390/s141222500
Received: 30 July 2014 / Revised: 18 October 2014 / Accepted: 14 November 2014 / Published: 27 November 2014
Cited by 8 | PDF Full-text (548 KB) | HTML Full-text | XML Full-text
Abstract
With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user’s needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, [...] Read more.
With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user’s needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view. Full article
Open AccessArticle Design of a Novel Flexible Capacitive Sensing Mattress for Monitoring Sleeping Respiratory
Sensors 2014, 14(11), 22021-22038; doi:10.3390/s141122021
Received: 5 September 2014 / Revised: 3 November 2014 / Accepted: 4 November 2014 / Published: 20 November 2014
PDF Full-text (1428 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, an algorithm to extract respiration signals using a flexible projected capacitive sensing mattress (FPCSM) designed for personal health assessment is proposed. Unlike the interfaces of conventional measurement systems for poly-somnography (PSG) and other alternative contemporary systems, the proposed FPCSM [...] Read more.
In this paper, an algorithm to extract respiration signals using a flexible projected capacitive sensing mattress (FPCSM) designed for personal health assessment is proposed. Unlike the interfaces of conventional measurement systems for poly-somnography (PSG) and other alternative contemporary systems, the proposed FPCSM uses projected capacitive sensing capability that is not worn or attached to the body. The FPCSM is composed of a multi-electrode sensor array that can not only observe gestures and motion behaviors, but also enables the FPCSM to function as a respiration monitor during sleep using the proposed approach. To improve long-term monitoring when body movement is possible, the FPCSM enables the selection of data from the sensing array, and the FPCSM methodology selects the electrodes with the optimal signals after the application of a channel reduction algorithm that counts the reversals in the capacitive sensing signals as a quality indicator. The simple algorithm is implemented in the time domain. The FPCSM system is used in experimental tests and is simultaneously compared with a commercial PSG system for verification. Multiple synchronous measurements are performed from different locations of body contact, and parallel data sets are collected. The experimental comparison yields a correlation coefficient of 0.88 between FPCSM and PSG, demonstrating the feasibility of the system design. Full article
Open AccessArticle Electrically Insulated Sensing of Respiratory Rate and Heartbeat Using Optical Fibers
Sensors 2014, 14(11), 21523-21534; doi:10.3390/s141121523
Received: 25 July 2014 / Revised: 12 September 2014 / Accepted: 9 October 2014 / Published: 14 November 2014
Cited by 2 | PDF Full-text (2205 KB) | HTML Full-text | XML Full-text
Abstract
Respiratory and heart rates are among the most important physiological parameters used to monitor patients’ health. It is important to design devices that can measure these parameters without risking or altering the subject’s health. In this context, a novel sensing method to [...] Read more.
Respiratory and heart rates are among the most important physiological parameters used to monitor patients’ health. It is important to design devices that can measure these parameters without risking or altering the subject’s health. In this context, a novel sensing method to monitor simultaneously the heartbeat and respiratory rate signals of patients within an electrically safety environment was developed and tested. An optical fiber-based sensor was used in order to detect two optical phenomena. Photo-plethysmography and the relation between bending radius and attenuation of optical fiber were coupled through a single beam light traveling along this fiber. Full article
Figures

Open AccessArticle PERFORM: A System for Monitoring, Assessment and Management of Patients with Parkinson’s Disease
Sensors 2014, 14(11), 21329-21357; doi:10.3390/s141121329
Received: 22 July 2014 / Revised: 25 September 2014 / Accepted: 20 October 2014 / Published: 11 November 2014
Cited by 9 | PDF Full-text (4765 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson’s disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals [...] Read more.
In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson’s disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients’ physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient’s motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient’s status on previous days and define the optimal therapeutical treatment. Full article
Figures

Open AccessReview Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors
Sensors 2014, 14(10), 19806-19842; doi:10.3390/s141019806
Received: 18 September 2014 / Revised: 10 October 2014 / Accepted: 16 October 2014 / Published: 22 October 2014
Cited by 18 | PDF Full-text (396 KB) | HTML Full-text | XML Full-text
Abstract
According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in [...] Read more.
According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people’s lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters. Full article
Open AccessArticle Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes
Sensors 2014, 14(10), 18583-18610; doi:10.3390/s141018583
Received: 21 July 2014 / Revised: 25 August 2014 / Accepted: 29 September 2014 / Published: 9 October 2014
Cited by 7 | PDF Full-text (2655 KB) | HTML Full-text | XML Full-text
Abstract
A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly [...] Read more.
A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system. Full article
Open AccessArticle Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks
Sensors 2014, 14(10), 18625-18649; doi:10.3390/s141018625
Received: 10 July 2014 / Revised: 23 September 2014 / Accepted: 29 September 2014 / Published: 9 October 2014
Cited by 25 | PDF Full-text (1137 KB) | HTML Full-text | XML Full-text
Abstract
Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and [...] Read more.
Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter) and complementary (Non-linear observer) filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles) and heading (yaw angle) errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided. Full article
Figures

Open AccessArticle Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals
Sensors 2014, 14(10), 18172-18186; doi:10.3390/s141018172
Received: 18 July 2014 / Revised: 16 September 2014 / Accepted: 17 September 2014 / Published: 29 September 2014
Cited by 4 | PDF Full-text (804 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed [...] Read more.
This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people. Full article
Open AccessArticle Novel Wearable and Wireless Ring-Type Pulse Oximeter with Multi-Detectors
Sensors 2014, 14(9), 17586-17599; doi:10.3390/s140917586
Received: 14 August 2014 / Revised: 9 September 2014 / Accepted: 17 September 2014 / Published: 19 September 2014
Cited by 4 | PDF Full-text (6961 KB) | HTML Full-text | XML Full-text
Abstract
The pulse oximeter is a popular instrument to monitor the arterial oxygen saturation (SPO2). Although a fingertip-type pulse oximeter is the mainstream one on the market at present, it is still inconvenient for long-term monitoring, in particular, with respect to [...] Read more.
The pulse oximeter is a popular instrument to monitor the arterial oxygen saturation (SPO2). Although a fingertip-type pulse oximeter is the mainstream one on the market at present, it is still inconvenient for long-term monitoring, in particular, with respect to motion. Therefore, the development of a wearable pulse oximeter, such as a finger base-type pulse oximeter, can effectively solve the above issue. However, the tissue structure of the finger base is complex, and there is lack of detailed information on the effect of the light source and detector placement on measuring SPO2. In this study, the practicability of a ring-type pulse oximeter with a multi-detector was investigated by optical human tissue simulation. The optimal design of a ring-type pulse oximeter that can provide the best efficiency of measuring SPO2 was discussed. The efficiency of ring-type pulse oximeters with a single detector and a multi-detector was also discussed. Finally, a wearable and wireless ring-type pulse oximeter was also implemented to validate the simulation results and was compared with the commercial fingertip-type pulse oximeter. Full article
Open AccessArticle Evaluation of Two Approaches for Aligning Data Obtained from a Motion Capture System and an In-Shoe Pressure Measurement System
Sensors 2014, 14(9), 16994-17007; doi:10.3390/s140916994
Received: 10 July 2014 / Revised: 13 August 2014 / Accepted: 11 September 2014 / Published: 12 September 2014
Cited by 1 | PDF Full-text (2636 KB) | HTML Full-text | XML Full-text
Abstract
An in-shoe pressure measurement (IPM) system can be used to measure center of pressure (COP) locations, and has fewer restrictions compared to the more conventional approach using a force platform. The insole of an IPM system, however, has its own coordinate system. [...] Read more.
An in-shoe pressure measurement (IPM) system can be used to measure center of pressure (COP) locations, and has fewer restrictions compared to the more conventional approach using a force platform. The insole of an IPM system, however, has its own coordinate system. To use an IPM system along with a motion capture system, there is thus a need to align the coordinate systems of the two measurement systems. To address this need, the current study examined two different approaches—rigid body transformation and nonlinear mapping (i.e., multilayer feed-forward neural network (MFNN))—to express COP measurements from an IPM system in the coordinate system of a motion capture system. Ten participants (five male and five female) completed several simulated manual material handling (MMH) activities, and during these activities the performance of the two approaches was assessed. Results indicated that: (1) performance varied between MMH activity types; and (2) a MFNN performed better than or comparable to the rigid body transformation, depending on the specific input variable sets used. Further, based on the results obtained, it was argued that a nonlinear mapping vs. rigid body transformation approach may be more effective to account for shoe deformation during MMH or potentially other types of physical activity. Full article
Open AccessArticle DIMETER: A Haptic Master Device for Tremor Diagnosis in Neurodegenerative Diseases
Sensors 2014, 14(3), 4536-4559; doi:10.3390/s140304536
Received: 18 December 2013 / Revised: 28 January 2014 / Accepted: 10 February 2014 / Published: 7 March 2014
PDF Full-text (582 KB) | HTML Full-text | XML Full-text
Abstract
In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The [...] Read more.
In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed. Full article
Open AccessArticle Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals
Sensors 2014, 14(2), 2052-2070; doi:10.3390/s140202052
Received: 2 December 2013 / Revised: 16 January 2014 / Accepted: 17 January 2014 / Published: 27 January 2014
Cited by 5 | PDF Full-text (1575 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when the muscle [...] Read more.
This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when the muscle is experiencing fatigue. This assumption is verified with a rigorous statistical analysis. Based on this proven linearity, localized muscular fatigue is simplified as a linear model. Furthermore, localized muscular fatigue is considered a dynamic process and, hence, the localized fatigue levels are tracked by updating the parameters with the most current surface electromyogram (sEMG) measurements. Finally, an overall fatigue level is computed by fusing localized muscular fatigue levels. The developed fatigue-tracking system is evaluated with two fatigue experiments (in which 10 male subjects and seven female subjects participated), including holding self-weight (dip start position training) and lifting weight with one arm (arm curl training). Full article
Figures

Open AccessArticle Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems
Sensors 2014, 14(2), 2036-2051; doi:10.3390/s140202036
Received: 2 December 2013 / Revised: 31 December 2013 / Accepted: 17 January 2014 / Published: 24 January 2014
Cited by 3 | PDF Full-text (375 KB) | HTML Full-text | XML Full-text
Abstract
The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it [...] Read more.
The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained. Full article
Open AccessArticle An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals
Sensors 2014, 14(1), 1474-1496; doi:10.3390/s140101474
Received: 14 December 2013 / Revised: 8 January 2014 / Accepted: 10 January 2014 / Published: 15 January 2014
Cited by 13 | PDF Full-text (1172 KB) | HTML Full-text | XML Full-text
Abstract
The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these [...] Read more.
The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types. Full article
Open AccessArticle A New Calibration Methodology for Thorax and Upper Limbs Motion Capture in Children Using Magneto and Inertial Sensors
Sensors 2014, 14(1), 1057-1072; doi:10.3390/s140101057
Received: 20 November 2013 / Revised: 3 December 2013 / Accepted: 5 December 2013 / Published: 9 January 2014
Cited by 7 | PDF Full-text (2642 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in wearable sensor technologies for motion capture have produced devices, mainly based on magneto and inertial measurement units (M-IMU), that are now suitable for out-of-the-lab use with children. In fact, the reduced size, weight and the wireless connectivity meet the [...] Read more.
Recent advances in wearable sensor technologies for motion capture have produced devices, mainly based on magneto and inertial measurement units (M-IMU), that are now suitable for out-of-the-lab use with children. In fact, the reduced size, weight and the wireless connectivity meet the requirement of minimum obtrusivity and give scientists the possibility to analyze children’s motion in daily life contexts. Typical use of magneto and inertial measurement units (M-IMU) motion capture systems is based on attaching a sensing unit to each body segment of interest. The correct use of this setup requires a specific calibration methodology that allows mapping measurements from the sensors’ frames of reference into useful kinematic information in the human limbs’ frames of reference. The present work addresses this specific issue, presenting a calibration protocol to capture the kinematics of the upper limbs and thorax in typically developing (TD) children. The proposed method allows the construction, on each body segment, of a meaningful system of coordinates that are representative of real physiological motions and that are referred to as functional frames (FFs). We will also present a novel cost function for the Levenberg–Marquardt algorithm, to retrieve the rotation matrices between each sensor frame (SF) and the corresponding FF. Reported results on a group of 40 children suggest that the method is repeatable and reliable, opening the way to the extensive use of this technology for out-of-the-lab motion capture in children. Full article
Figures

Open AccessArticle On the Capability of Smartphones to Perform as Communication Gateways in Medical Wireless Personal Area Networks
Sensors 2014, 14(1), 575-594; doi:10.3390/s140100575
Received: 16 November 2013 / Revised: 16 December 2013 / Accepted: 29 December 2013 / Published: 2 January 2014
Cited by 6 | PDF Full-text (1339 KB) | HTML Full-text | XML Full-text
Abstract
This paper evaluates and characterizes the technical performance of medical wireless personal area networks (WPANs) that are based on smartphones. For this purpose, a prototype of a health telemonitoring system is presented. The prototype incorporates a commercial Android smartphone, which acts as [...] Read more.
This paper evaluates and characterizes the technical performance of medical wireless personal area networks (WPANs) that are based on smartphones. For this purpose, a prototype of a health telemonitoring system is presented. The prototype incorporates a commercial Android smartphone, which acts as a relay point, or “gateway”, between a set of wireless medical sensors and a data server. Additionally, the paper investigates if the conventional capabilities of current commercial smartphones can be affected by their useas gateways or “Holters” in health monitoring applications. Specifically, the profiling has focused on the CPU and power consumption of the mobile devices. These metrics have been measured under several test conditions modifying the smartphone model, the type of sensors connected to the WPAN, the employed Bluetooth profile (SPP (serial port profile) or HDP (health device profile)), the use of other peripherals, such as a GPS receiver, the impact of the use of the Wi-Fi interface or the employed method to encode and forward the data that are collected from the sensors. Full article
Open AccessReview Technological Solutions and Main Indices for the Assessment of Newborns’ Nutritive Sucking: A Review
Sensors 2014, 14(1), 634-658; doi:10.3390/s140100634
Received: 17 October 2013 / Revised: 6 December 2013 / Accepted: 17 December 2013 / Published: 2 January 2014
Cited by 11 | PDF Full-text (1098 KB) | HTML Full-text | XML Full-text
Abstract
Nutritive Sucking (NS) is a highly organized process that is essential for infants’ feeding during the first six months of their life. It requires the complex coordination of sucking, swallowing and breathing. The infant’s inability to perform a safe and successful oral [...] Read more.
Nutritive Sucking (NS) is a highly organized process that is essential for infants’ feeding during the first six months of their life. It requires the complex coordination of sucking, swallowing and breathing. The infant’s inability to perform a safe and successful oral feeding can be an early detector of immaturity of the Central Nervous System (CNS). Even though the importance of early sucking measures has been confirmed over the years, the need for standardized instrumental assessment tools still exists. Clinicians would benefit from specifically designed devices to assess oral feeding ability in their routine clinical monitoring and decision-making process. This work is a review of the main instrumental solutions developed to assess an infant’s NS behavior, with a detailed survey of the main quantities and indices measured and/or estimated to characterize sucking behavior skills and their development. The adopted sensing measuring systems will be described, and their main advantages and weaknesses will be discussed, taking into account their application to clinical practice, or to at-home monitoring as post-discharge assessment tools. Finally, the study will highlight the most suitable sensing solutions and give some prompts for further research. Full article

2013

Jump to: 2016, 2015, 2014

Open AccessArticle A Modular Sensorized Mat for Monitoring Infant Posture
Sensors 2014, 14(1), 510-531; doi:10.3390/s140100510
Received: 23 October 2013 / Revised: 11 December 2013 / Accepted: 17 December 2013 / Published: 31 December 2013
Cited by 5 | PDF Full-text (924 KB) | HTML Full-text | XML Full-text
Abstract
We present a novel sensorized mat for monitoring infant’s posture through the measure of pressure maps. The pressure-sensitive mat is based on an optoelectronic technology developed in the last few years at Scuola Superiore Sant’Anna: a soft silicone skin cover, which constitutes [...] Read more.
We present a novel sensorized mat for monitoring infant’s posture through the measure of pressure maps. The pressure-sensitive mat is based on an optoelectronic technology developed in the last few years at Scuola Superiore Sant’Anna: a soft silicone skin cover, which constitutes the mat, participates in the transduction principle and provides the mat with compliance. The device has a modular structure (with a minimum of one and a maximum of six sub-modules, and a total surface area of about 1 m2) that enables dimensional adaptation of the pressure-sensitive area to different specific applications. The system consists of on-board electronics for data collection, pre-elaboration, and transmission to a remote computing unit for analysis and posture classification. In this work we present a complete description of the sensing apparatus along with its experimental characterization and validation with five healthy infants. Full article
Figures

Open AccessArticle A Novel Assessment of Flexibility by Microcirculatory Signals
Sensors 2014, 14(1), 478-491; doi:10.3390/s140100478
Received: 15 October 2013 / Revised: 3 December 2013 / Accepted: 17 December 2013 / Published: 30 December 2013
PDF Full-text (404 KB) | HTML Full-text | XML Full-text
Abstract
Flexibility testing is one of the most important fitness assessments. It is generally evaluated by measuring the range of motion (RoM) of body segments around a joint center. This study presents a novel assessment of flexibility in the microcirculatory aspect. Eighteen college [...] Read more.
Flexibility testing is one of the most important fitness assessments. It is generally evaluated by measuring the range of motion (RoM) of body segments around a joint center. This study presents a novel assessment of flexibility in the microcirculatory aspect. Eighteen college students were recruited for the flexibility assessment. The flexibility of the leg was defined according to the angle of active ankle dorsiflexion measured by goniometry. Six legs were excluded, and the remaining thirty legs were categorized into two groups, group H (n = 15 with higher flexibility) and group L (n = 15 with lower flexibility), according to their RoM. The microcirculatory signals of the gastrocnemius muscle on the belly were monitored by using Laser-Doppler Flowmetry (LDF) with a noninvasive skin probe. Three indices of nonpulsatile component (DC), pulsatile component (AC) and perfusion pulsatility (PP) were defined from the LDF signals after signal processing. The results revealed that both the DC and AC values of the group H that demonstrated higher stability underwent muscle stretching. In contrast, these indices of group L had interferences and became unstable during muscle stretching. The PP value of group H was a little higher than that of group L. These primary findings help us to understand the microcirculatory physiology of flexibility, and warrant further investigations for use of non-invasive LDF techniques in the assessment of flexibility. Full article
Open AccessReview Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges
Sensors 2013, 13(12), 17472-17500; doi:10.3390/s131217472
Received: 20 September 2013 / Revised: 15 November 2013 / Accepted: 6 December 2013 / Published: 17 December 2013
Cited by 38 | PDF Full-text (493 KB) | HTML Full-text | XML Full-text
Abstract
The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels [...] Read more.
The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems. Full article
Figures

Open AccessArticle Combined Hand Gesture — Speech Model for Human Action Recognition
Sensors 2013, 13(12), 17098-17129; doi:10.3390/s131217098
Received: 15 October 2013 / Revised: 2 December 2013 / Accepted: 6 December 2013 / Published: 12 December 2013
PDF Full-text (2111 KB) | HTML Full-text | XML Full-text
Abstract
This study proposes a dynamic hand gesture detection technology to effectively detect dynamic hand gesture areas, and a hand gesture recognition technology to improve the dynamic hand gesture recognition rate. Meanwhile, the corresponding relationship between state sequences in hand gesture and speech [...] Read more.
This study proposes a dynamic hand gesture detection technology to effectively detect dynamic hand gesture areas, and a hand gesture recognition technology to improve the dynamic hand gesture recognition rate. Meanwhile, the corresponding relationship between state sequences in hand gesture and speech models is considered by integrating speech recognition technology with a multimodal model, thus improving the accuracy of human behavior recognition. The experimental results proved that the proposed method can effectively improve human behavior recognition accuracy and the feasibility of system applications. Experimental results verified that the multimodal gesture-speech model provided superior accuracy when compared to the single modal versions. Full article
Open AccessArticle Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home
Sensors 2013, 13(12), 16985-17005; doi:10.3390/s131216985
Received: 19 September 2013 / Revised: 22 November 2013 / Accepted: 26 November 2013 / Published: 10 December 2013
Cited by 4 | PDF Full-text (2270 KB) | HTML Full-text | XML Full-text
Abstract
Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers [...] Read more.
Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second. Full article
Figures

Open AccessArticle Automatic and Objective Assessment of Alternating Tapping Performance in Parkinson’s Disease
Sensors 2013, 13(12), 16965-16984; doi:10.3390/s131216965
Received: 26 September 2013 / Revised: 21 November 2013 / Accepted: 5 December 2013 / Published: 9 December 2013
Cited by 5 | PDF Full-text (870 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a [...] Read more.
This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson’s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson’s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping. Full article
Open AccessArticle Design and Evaluation of a Low-Cost Smartphone Pulse Oximeter
Sensors 2013, 13(12), 16882-16893; doi:10.3390/s131216882
Received: 15 October 2013 / Revised: 16 November 2013 / Accepted: 2 December 2013 / Published: 6 December 2013
Cited by 12 | PDF Full-text (550 KB) | HTML Full-text | XML Full-text
Abstract
Infectious diseases such as pneumonia take the lives of millions of children in low- and middle-income countries every year. Many of these deaths could be prevented with the availability of robust and low-cost diagnostic tools using integrated sensor technology. Pulse oximetry in [...] Read more.
Infectious diseases such as pneumonia take the lives of millions of children in low- and middle-income countries every year. Many of these deaths could be prevented with the availability of robust and low-cost diagnostic tools using integrated sensor technology. Pulse oximetry in particular, offers a unique non-invasive and specific test for an increase in the severity of many infectious diseases such as pneumonia. If pulse oximetry could be delivered on widely available mobile phones, it could become a compelling solution to global health challenges. Many lives could be saved if this technology was disseminated effectively in the affected regions of the world to rescue patients from the fatal consequences of these infectious diseases. We describe the implementation of such an oximeter that interfaces a conventional clinical oximeter finger sensor with a smartphone through the headset jack audio interface, and present a simulator-based systematic verification system to be used for automated validation of the sensor interface on different smartphones and media players. An excellent agreement was found between the simulator and the audio oximeter for both oxygen saturation and heart rate over a wide range of optical transmission levels on 4th and 5th generations of the iPod TouchTM and iPhoneTM devices. Full article
Open AccessArticle Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network
Sensors 2013, 13(11), 15613-15632; doi:10.3390/s131115613
Received: 11 September 2013 / Revised: 12 October 2013 / Accepted: 12 October 2013 / Published: 15 November 2013
Cited by 2 | PDF Full-text (2948 KB) | HTML Full-text | XML Full-text
Abstract
Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices [...] Read more.
Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal. Full article
Figures

Open AccessReview Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition
Sensors 2013, 13(11), 14918-14953; doi:10.3390/s131114918
Received: 1 September 2013 / Revised: 10 October 2013 / Accepted: 25 October 2013 / Published: 1 November 2013
Cited by 2 | PDF Full-text (1396 KB) | HTML Full-text | XML Full-text
Abstract
Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during [...] Read more.
Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals. Full article
Open AccessArticle Comparison of Systolic Blood Pressure Values Obtained by Photoplethysmography and by Korotkoff Sounds
Sensors 2013, 13(11), 14797-14812; doi:10.3390/s131114797
Received: 2 September 2013 / Revised: 8 October 2013 / Accepted: 25 October 2013 / Published: 31 October 2013
Cited by 4 | PDF Full-text (193 KB) | HTML Full-text | XML Full-text
Abstract
In the current study, a non-invasive technique for systolic blood pressure (SBP) measurement based on the detection of photoplethysmographic (PPG) pulses during pressure-cuff deflation was compared to sphygmomanometry—the Korotkoff sounds technique. The PPG pulses disappear for cuff-pressures above the SBP value and [...] Read more.
In the current study, a non-invasive technique for systolic blood pressure (SBP) measurement based on the detection of photoplethysmographic (PPG) pulses during pressure-cuff deflation was compared to sphygmomanometry—the Korotkoff sounds technique. The PPG pulses disappear for cuff-pressures above the SBP value and reappear when the cuff-pressure decreases below the SBP value. One hundred and twenty examinations were performed on forty subjects. In 97 examinations the two methods differed by less than 3 mmHg. In nine examinations the SBP value measured by PPG was higher than that measured by sphygmomanometry by 5 mmHg or more. In only one examination the former was lower by 5 mmHg or more than the latter. The appearance of either the PPG pulses or the Korotkoff sounds assures that the artery under the cuff is open during systolic peak pressure. In the nine examinations mentioned above the PPG pulses were observed while Korotkoff sounds were not detected, despite the open artery during systole. In these examinations, the PPG-based technique was more reliable than sphygmomanometry. The high signal-to-noise ratio of measured PPG pulses indicates that automatic measurement of the SBP by means of automatic detection of the PPG signals is feasible. Full article
Open AccessArticle A Wearable Inertial Measurement Unit for Long-Term Monitoring in the Dependency Care Area
Sensors 2013, 13(10), 14079-14104; doi:10.3390/s131014079
Received: 12 July 2013 / Revised: 27 September 2013 / Accepted: 29 September 2013 / Published: 18 October 2013
Cited by 12 | PDF Full-text (464 KB) | HTML Full-text | XML Full-text
Abstract
Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices [...] Read more.
Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMU’s movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A µSD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinson’s disease symptoms, in gait analysis, and in a fall detection system. Full article
Figures

Open AccessArticle On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation
Sensors 2013, 13(10), 13609-13623; doi:10.3390/s131013609
Received: 15 July 2013 / Revised: 2 September 2013 / Accepted: 24 September 2013 / Published: 10 October 2013
Cited by 4 | PDF Full-text (476 KB) | HTML Full-text | XML Full-text
Abstract
The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate [...] Read more.
The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate the systolic and diastolic pressures. This paper proposes a Bayesian model to estimate the systolic and diastolic ratios. These ratios are an improvement over the single fixed systolic and diastolic ratios used in the algorithms that are available in the literature. The proposed method shows lower mean difference (MD) with standard deviation (SD) compared to the MAA for both SBP and DBP consistently in all the five measurements. Full article
Open AccessCommunication Autonomic Nervous System Responses Can Reveal Visual Fatigue Induced by 3D Displays
Sensors 2013, 13(10), 13054-13062; doi:10.3390/s131013054
Received: 14 August 2013 / Revised: 17 September 2013 / Accepted: 17 September 2013 / Published: 26 September 2013
Cited by 9 | PDF Full-text (367 KB) | HTML Full-text | XML Full-text
Abstract
Previous research has indicated that viewing 3D displays may induce greater visual fatigue than viewing 2D displays. Whether viewing 3D displays can evoke measureable emotional responses, however, is uncertain. In the present study, we examined autonomic nervous system responses in subjects viewing [...] Read more.
Previous research has indicated that viewing 3D displays may induce greater visual fatigue than viewing 2D displays. Whether viewing 3D displays can evoke measureable emotional responses, however, is uncertain. In the present study, we examined autonomic nervous system responses in subjects viewing 2D or 3D displays. Autonomic responses were quantified in each subject by heart rate, galvanic skin response, and skin temperature. Viewers of both 2D and 3D displays showed strong positive correlations with heart rate, which indicated little differences between groups. In contrast, galvanic skin response and skin temperature showed weak positive correlations with average difference between viewing 2D and 3D. We suggest that galvanic skin response and skin temperature can be used to measure and compare autonomic nervous responses in subjects viewing 2D and 3D displays. Full article
Open AccessReview Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations
Sensors 2013, 13(10), 12852-12902; doi:10.3390/s131012852
Received: 19 July 2013 / Revised: 2 September 2013 / Accepted: 10 September 2013 / Published: 25 September 2013
Cited by 11 | PDF Full-text (962 KB) | HTML Full-text | XML Full-text
Abstract
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical [...] Read more.
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions. Full article
Open AccessArticle Laser Doppler Blood Flow Imaging Using a CMOS Imaging Sensor with On-Chip Signal Processing
Sensors 2013, 13(9), 12632-12647; doi:10.3390/s130912632
Received: 25 July 2013 / Revised: 6 September 2013 / Accepted: 9 September 2013 / Published: 18 September 2013
Cited by 8 | PDF Full-text (2117 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The first fully integrated 2D CMOS imaging sensor with on-chip signal processing for applications in laser Doppler blood flow (LDBF) imaging has been designed and tested. To obtain a space efficient design over 64 × 64 pixels means that standard processing electronics [...] Read more.
The first fully integrated 2D CMOS imaging sensor with on-chip signal processing for applications in laser Doppler blood flow (LDBF) imaging has been designed and tested. To obtain a space efficient design over 64 × 64 pixels means that standard processing electronics used off-chip cannot be implemented. Therefore the analog signal processing at each pixel is a tailored design for LDBF signals with balanced optimization for signal-to-noise ratio and silicon area. This custom made sensor offers key advantages over conventional sensors, viz. the analog signal processing at the pixel level carries out signal normalization; the AC amplification in combination with an anti-aliasing filter allows analog-to-digital conversion with a low number of bits; low resource implementation of the digital processor enables on-chip processing and the data bottleneck that exists between the detector and processing electronics has been overcome. The sensor demonstrates good agreement with simulation at each design stage. The measured optical performance of the sensor is demonstrated using modulated light signals and in vivo blood flow experiments. Images showing blood flow changes with arterial occlusion and an inflammatory response to a histamine skin-prick demonstrate that the sensor array is capable of detecting blood flow signals from tissue. Full article
Open AccessCommunication The Capability of Fiber Bragg Grating Sensors to Measure Amputees’ Trans-Tibial Stump/Socket Interface Pressures
Sensors 2013, 13(8), 10348-10357; doi:10.3390/s130810348
Received: 4 June 2013 / Revised: 5 August 2013 / Accepted: 7 August 2013 / Published: 12 August 2013
Cited by 4 | PDF Full-text (878 KB) | HTML Full-text | XML Full-text
Abstract
This study presents the first investigation into the capability of fiber Bragg grating (FBG) sensors to measure interface pressure between the stump and the prosthetic sockets of a trans-tibial amputee. FBG element(s) were recoated with and embedded in a thin layer of [...] Read more.
This study presents the first investigation into the capability of fiber Bragg grating (FBG) sensors to measure interface pressure between the stump and the prosthetic sockets of a trans-tibial amputee. FBG element(s) were recoated with and embedded in a thin layer of epoxy material to form a sensing pad, which was in turn embedded in a silicone polymer material to form a pressure sensor. The sensor was tested in real time by inserting a heavy-duty balloon into the socket and inflating it by using an air compressor. This test was conducted to examine the sensitivity and repeatability of the sensor when subjected to pressure from the stump of the trans-tibial amputee and to mimic the actual environment of the amputee’s Patellar Tendon (PT) bar. The sensor exhibited a sensitivity of 127 pm/N and a maximum FSO hysteresis of around ~0.09 in real-time operation. Very good reliability was achieved when the sensor was utilized for in situ measurements. This study may lead to smart FBG-based amputee stump/socket structures for pressure monitoring in amputee socket systems, which will result in better-designed prosthetic sockets that ensure improved patient satisfaction. Full article
Open AccessArticle Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation
Sensors 2013, 13(6), 7735-7755; doi:10.3390/s130607735
Received: 23 April 2013 / Revised: 21 May 2013 / Accepted: 13 June 2013 / Published: 17 June 2013
Cited by 7 | PDF Full-text (17714 KB) | HTML Full-text | XML Full-text
Abstract
Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, [...] Read more.
Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation. Full article
Figures

Open AccessArticle Soft Stethoscope for Detecting Asthma Wheeze in Young Children
Sensors 2013, 13(6), 7399-7413; doi:10.3390/s130607399
Received: 8 April 2013 / Revised: 20 May 2013 / Accepted: 3 June 2013 / Published: 6 June 2013
Cited by 5 | PDF Full-text (482 KB) | HTML Full-text | XML Full-text
Abstract
Asthma is a chronic disease that is commonly suffered by children. Asthmatic children have a lower quality of life than other children. Physicians and pediatricians recommend that parents record the frequency of attacks and their symptoms to help manage their children’s asthma. [...] Read more.
Asthma is a chronic disease that is commonly suffered by children. Asthmatic children have a lower quality of life than other children. Physicians and pediatricians recommend that parents record the frequency of attacks and their symptoms to help manage their children’s asthma. However, the lack of a convenient device for monitoring the asthmatic condition leads to the difficulties in managing it, especially when it is suffered by young children. This work develops a wheeze detection system for use at home. A small and soft stethoscope was used to collect the respiratory sound. The wheeze detection algorithm was the Adaptive Respiratory Spectrum Correlation Coefficient (RSACC) algorithm, which has the advantages of high sensitivity/specificity and a low computational requirement. Fifty-nine sound files from eight young children (one to seven years old) were collected in the emergency room and analyzed. The results revealed that the system provided 88% sensitivity and 94% specificity in wheeze detection. In conclusion, this small soft stethoscope can be easily used on young children. A noisy environment does not affect the effectiveness of the system in detecting wheeze. Hence, the system can be used at home by parents who wish to evaluate and manage the asthmatic condition of their children. Full article

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
sensors@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Sensors
Back to Top