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Special Issue "Sensing Technology for Healthcare System"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 October 2016).

Special Issue Editors

Guest Editor
Prof. Dr. Octavian Postolache

1 Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
2 Escola de Tecnologias e Arquitetura (ISTA), ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
Website | E-Mail
Interests: smart sensors; wireless sensors network; test and automated instrumentation for IoT; unobtrusive sensing for cardio-respiratory monitoring; smart systems for physical rehabilitation; standards for WSN and IoT; sensors for environment monitoring; IoT for smart ports and smart cities
Guest Editor
Dr. Alex Casson

University of Manchester, UK
Website | E-Mail
Interests: printable electrodes; low power real-time signal processing; bioelectronics; neurotechnology for real-time feedback to the user
Co-Guest Editor
Prof. Dr. Subhas Mukhopadhyay

School of Engineering, Macquarie University, NSW 2109, Australia
Website | E-Mail
Interests: smart sensors; sensor networks; wireless sensor networks; Internet of Things; sensors modeling and applications; smart homes; smart city

Special Issue Information

Dear Colleagues,

The advances in the fields of smart materials, sensors, low power electronics and power harvesting, as with in information and communication technology has stimulated the application of these technologies in medical and healthcare domains. From simple healthcare devices to intelligent distributed healthcare systems, accurate detection and early warning of health condition for users in interaction with different living scenarios represent one of the main requirements. The novel unobtrusive sensing solutions expressed by smart wearable or smart objects provide users’ health status information without disturbing daily activities for long-term period cardio-respiratory and motor activity assessment. The latest research efforts focus on wearable and implantable sensing systems that require extreme miniaturization; alternative solutions promote the invisibility for higher acceptance rate by the users by concealing sensors into daily used objects, such as furniture or walking aids.

This Special Issue invites high-quality research articles and review articles on sensing technologies, sensor electronics, and interfaces working towards sensor integration in smart environments and health and social care services. Monitoring physiological parameters and motor activity based on unobtrusive sensing and pervasive computing, highlighting the specific healthcare system interactions, are particular topics of interest.

Papers are solicited in, but are not limited to, the following and related topics:

  • New sensor materials and technologies for medical applications
  • Printed, flexible, biodegradable and biocompatible electronics
  • Sensor devices and sensor arrays
  • Nano sensors
  • Electrical and thermal-based sensors
  • Wearable and implantable sensors for biomedical applications
  • Remote sensing systems for healthcare
  • Novel electronics for brain activity monitoring
  • Sensors and Systems for Brain Computer Interfaces
  • Sensors and Systems for Physical Rehabilitation

Dr. Octavian Adrian Postolache
Dr. Alex Casson
Guest Editors

Prof. Subhas Mukhopadhyay
Co-Guest Editor

Manuscript Submission Information

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. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind 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 semimonthly 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). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart sensors
  • Sensor materials
  • Sensor devices and sensor arrays
  • Wearable sensors
  • Implantable sensors
  • Remote sensing
  • Unobtrusive sensing
  • Brain activity monitoring

Published Papers (27 papers)

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Open AccessArticle
Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
Sensors 2017, 17(3), 582; https://doi.org/10.3390/s17030582
Received: 8 December 2016 / Revised: 6 February 2017 / Accepted: 23 February 2017 / Published: 13 March 2017
Cited by 6 | PDF Full-text (6504 KB) | HTML Full-text | XML Full-text
Abstract
Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement [...] Read more.
Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures
Sensors 2017, 17(3), 481; https://doi.org/10.3390/s17030481
Received: 21 November 2016 / Revised: 6 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
Cited by 5 | PDF Full-text (3224 KB) | HTML Full-text | XML Full-text
Abstract
Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS [...] Read more.
Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts
Sensors 2017, 17(2), 385; https://doi.org/10.3390/s17020385
Received: 23 December 2016 / Revised: 4 February 2017 / Accepted: 12 February 2017 / Published: 16 February 2017
Cited by 10 | PDF Full-text (1215 KB) | HTML Full-text | XML Full-text
Abstract
The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach [...] Read more.
The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Preliminary Study for Designing a Novel Vein-Visualizing Device
Sensors 2017, 17(2), 304; https://doi.org/10.3390/s17020304
Received: 31 October 2016 / Revised: 18 January 2017 / Accepted: 3 February 2017 / Published: 7 February 2017
Cited by 8 | PDF Full-text (11539 KB) | HTML Full-text | XML Full-text
Abstract
Venipuncture is an important health diagnosis process. Although venipuncture is one of the most commonly performed procedures in medical environments, locating the veins of infants, obese, anemic, or colored patients is still an arduous task even for skilled practitioners. To solve this problem, [...] Read more.
Venipuncture is an important health diagnosis process. Although venipuncture is one of the most commonly performed procedures in medical environments, locating the veins of infants, obese, anemic, or colored patients is still an arduous task even for skilled practitioners. To solve this problem, several devices using infrared light have recently become commercially available. However, such devices for venipuncture share a common drawback, especially when visualizing deep veins or veins of a thick part of the body like the cubital fossa. This paper proposes a new vein-visualizing device applying a new penetration method using near-infrared (NIR) light. The light module is attached directly on to the declared area of the skin. Then, NIR beam is rayed from two sides of the light module to the vein with a specific angle. This gives a penetration effect. In addition, through an image processing procedure, the vein structure is enhanced to show it more accurately. Through a phantom study, the most effective penetration angle of the NIR module is decided. Additionally, the feasibility of the device is verified through experiments in vivo. The prototype allows us to visualize the vein patterns of thicker body parts, such as arms. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I
Sensors 2017, 17(1), 211; https://doi.org/10.3390/s17010211
Received: 2 November 2016 / Revised: 16 January 2017 / Accepted: 17 January 2017 / Published: 22 January 2017
Cited by 24 | PDF Full-text (1944 KB) | HTML Full-text | XML Full-text
Abstract
Commercial self-monitoring devices are becoming increasingly popular, and over the last decade, the use of self-monitoring technology has spread widely in both consumer and medical markets. The purpose of this study was to evaluate five commercially available self-monitoring devices for further testing in [...] Read more.
Commercial self-monitoring devices are becoming increasingly popular, and over the last decade, the use of self-monitoring technology has spread widely in both consumer and medical markets. The purpose of this study was to evaluate five commercially available self-monitoring devices for further testing in clinical applications. Four activity trackers and one sleep tracker were evaluated based on step count validity and heart rate validity. Methods: The study enrolled 22 healthy volunteers in a walking test. Volunteers walked a 100 m track at 2 km/h and 3.5 km/h. Steps were measured by four activity trackers and compared to gyroscope readings. Two trackers were also tested on nine subjects by comparing pulse readings to Holter monitoring. Results: The lowest average systematic error in the walking tests was −0.2%, recorded on the Garmin Vivofit 2 at 3.5 km/h; the highest error was the Fitbit Charge HR at 2 km/h with an error margin of 26.8%. Comparisons of pulse measurements from the Fitbit Charge HR revealed a margin error of −3.42% ± 7.99% compared to the electrocardiogram. The Beddit sleep tracker measured a systematic error of −3.27% ± 4.60%. Conclusion: The measured results revealed the current functionality and limitations of the five self-tracking devices, and point towards a need for future research in this area. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition
Sensors 2017, 17(1), 66; https://doi.org/10.3390/s17010066
Received: 1 November 2016 / Revised: 20 December 2016 / Accepted: 27 December 2016 / Published: 30 December 2016
Cited by 14 | PDF Full-text (1240 KB) | HTML Full-text | XML Full-text
Abstract
Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances [...] Read more.
Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called “deep learning”, which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google’s TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold starts and big data processing of physical activity records. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Concept and Development of an Electronic Framework Intended for Electrode and Surrounding Environment Characterization In Vivo
Sensors 2017, 17(1), 59; https://doi.org/10.3390/s17010059
Received: 15 October 2016 / Revised: 22 December 2016 / Accepted: 24 December 2016 / Published: 30 December 2016
Cited by 1 | PDF Full-text (10748 KB) | HTML Full-text | XML Full-text
Abstract
There has been substantial progress over the last decade towards miniaturizing implantable microelectrodes for use in Active Implantable Medical Devices (AIMD). Compared to the rapid development and complexity of electrode miniaturization, methods to monitor and assess functional integrity and electrical functionality of these [...] Read more.
There has been substantial progress over the last decade towards miniaturizing implantable microelectrodes for use in Active Implantable Medical Devices (AIMD). Compared to the rapid development and complexity of electrode miniaturization, methods to monitor and assess functional integrity and electrical functionality of these electrodes, particularly during long term stimulation, have not progressed to the same extent. Evaluation methods that form the gold standard, such as stimulus pulse testing, cyclic voltammetry and electrochemical impedance spectroscopy, are either still bound to laboratory infrastructure (impractical for long term in vivo experiments) or deliver no comprehensive insight into the material’s behaviour. As there is a lack of cost effective and practical predictive measures to understand long term electrode behaviour in vivo, material investigations need to be performed after explantation of the electrodes. We propose the analysis of the electrode and its environment in situ, to better understand and correlate the effects leading to electrode failure. The derived knowledge shall eventually lead to improved electrode designs, increased electrode functionality and safety in clinical applications. In this paper, the concept, design and prototyping of a sensor framework used to analyse the electrode’s behaviour and to monitor diverse electrode failure mechanisms, even during stimulation pulses, is presented. We focused on the electronic circuitry and data acquisition techniques required for a conceptual multi-sensor system. Functionality of single modules and a prototype framework have been demonstrated, but further work is needed to convert the prototype system into an implantable device. In vitro studies will be conducted first to verify sensor performance and reliability. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Sensing Technologies for Autism Spectrum Disorder Screening and Intervention
Sensors 2017, 17(1), 46; https://doi.org/10.3390/s17010046
Received: 3 August 2016 / Revised: 15 December 2016 / Accepted: 16 December 2016 / Published: 27 December 2016
Cited by 11 | PDF Full-text (17523 KB) | HTML Full-text | XML Full-text
Abstract
This paper reviews the state-of-the-art in sensing technologies that are relevant for Autism Spectrum Disorder (ASD) screening and therapy. This disorder is characterized by difficulties in social communication, social interactions, and repetitive behaviors. It is diagnosed during the first three years of life. [...] Read more.
This paper reviews the state-of-the-art in sensing technologies that are relevant for Autism Spectrum Disorder (ASD) screening and therapy. This disorder is characterized by difficulties in social communication, social interactions, and repetitive behaviors. It is diagnosed during the first three years of life. Early and intensive interventions have been shown to improve the developmental trajectory of the affected children. The earlier the diagnosis, the sooner the intervention therapy can begin, thus, making early diagnosis an important research goal. Technological innovations have tremendous potential to assist with early diagnosis and improve intervention programs. The need for careful and methodological evaluation of such emerging technologies becomes important in order to assist not only the therapists and clinicians in their selection of suitable tools, but to also guide the developers of the technologies in improving hardware and software. In this paper, we survey the literatures on sensing technologies for ASD and we categorize them into eye trackers, movement trackers, electrodermal activity monitors, tactile sensors, vocal prosody and speech detectors, and sleep quality assessment devices. We assess their effectiveness and study their limitations. We also examine the challenges faced by this growing field that need to be addressed before these technologies can perform up to their theoretical potential. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems
Sensors 2016, 16(12), 2142; https://doi.org/10.3390/s16122142
Received: 14 October 2016 / Revised: 8 December 2016 / Accepted: 13 December 2016 / Published: 15 December 2016
Cited by 5 | PDF Full-text (352 KB) | HTML Full-text | XML Full-text
Abstract
Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in [...] Read more.
Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
An Open Platform for Seamless Sensor Support in Healthcare for the Internet of Things
Sensors 2016, 16(12), 2089; https://doi.org/10.3390/s16122089
Received: 22 October 2016 / Revised: 25 November 2016 / Accepted: 3 December 2016 / Published: 8 December 2016
Cited by 14 | PDF Full-text (8124 KB) | HTML Full-text | XML Full-text
Abstract
Population aging and increasing pressure on health systems are two issues that demand solutions. Involving and empowering citizens as active managers of their health represents a desirable shift from the current culture mainly focused on treatment of disease, to one also focused on [...] Read more.
Population aging and increasing pressure on health systems are two issues that demand solutions. Involving and empowering citizens as active managers of their health represents a desirable shift from the current culture mainly focused on treatment of disease, to one also focused on continuous health management and well-being. Current developments in technological areas such as the Internet of Things (IoT), lead to new technological solutions that can aid this shift in the healthcare sector. This study presents the design, development, implementation and evaluation of a platform called Common Recognition and Identification Platform (CRIP), a part of the CareStore project, which aims at supporting caregivers and citizens to manage health routines in a seamless way. Specifically, the CRIP offers sensor-based support for seamless identification of users and health devices. A set of initial requirements was defined with a focus on usability limitations and current sensor technologies. The CRIP was designed and implemented using several technologies that enable seamless integration and interaction of sensors and people, namely Near Field Communication and fingerprint biometrics for identification and authentication, Bluetooth for communication with health devices and web services for wider integration with other platforms. Two CRIP prototypes were implemented and evaluated in laboratory during a period of eight months. The evaluations consisted of identifying users and devices, as well as seamlessly configure and acquire vital data from the last. Also, the entire Carestore platform was deployed in a nursing home where its usability was evaluated with caregivers. The evaluations helped assess that seamless identification of users and seamless configuration and communication with health devices is feasible and can help enable the IoT on healthcare applications. Therefore, the CRIP and similar platforms could be transformed into a valuable enabling technology for secure and reliable IoT deployments on the healthcare sector. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar
Sensors 2016, 16(12), 2025; https://doi.org/10.3390/s16122025
Received: 9 August 2016 / Revised: 10 November 2016 / Accepted: 24 November 2016 / Published: 30 November 2016
Cited by 20 | PDF Full-text (6081 KB) | HTML Full-text | XML Full-text
Abstract
The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler [...] Read more.
The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia
Sensors 2016, 16(12), 1989; https://doi.org/10.3390/s16121989
Received: 3 August 2016 / Revised: 4 November 2016 / Accepted: 18 November 2016 / Published: 24 November 2016
Cited by 12 | PDF Full-text (2871 KB) | HTML Full-text | XML Full-text
Abstract
Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This [...] Read more.
Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of “Stressed” and “Not stressed” for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Real-Time Kinect Signature-Based Patient Home Monitoring System
Sensors 2016, 16(11), 1965; https://doi.org/10.3390/s16111965
Received: 9 August 2016 / Revised: 10 November 2016 / Accepted: 12 November 2016 / Published: 23 November 2016
Cited by 15 | PDF Full-text (5341 KB) | HTML Full-text | XML Full-text
Abstract
Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human [...] Read more.
Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints’ estimations, and erroneous multiplexing of different subjects’ estimations to one. This study addresses these limitations by incorporating a set of features that create a unique “Kinect Signature”. The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Smart Toys Designed for Detecting Developmental Delays
Sensors 2016, 16(11), 1953; https://doi.org/10.3390/s16111953
Received: 7 September 2016 / Revised: 2 November 2016 / Accepted: 16 November 2016 / Published: 20 November 2016
Cited by 4 | PDF Full-text (7492 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we describe the design considerations and implementation of a smart toy system, a technology for supporting the automatic recording and analysis for detecting developmental delays recognition when children play using the smart toy. To achieve this goal, we take advantage [...] Read more.
In this paper, we describe the design considerations and implementation of a smart toy system, a technology for supporting the automatic recording and analysis for detecting developmental delays recognition when children play using the smart toy. To achieve this goal, we take advantage of the current commercial sensor features (reliability, low consumption, easy integration, etc.) to develop a series of sensor-based low-cost devices. Specifically, our prototype system consists of a tower of cubes augmented with wireless sensing capabilities and a mobile computing platform that collect the information sent from the cubes allowing the later analysis by childhood development professionals in order to verify a normal behaviour or to detect a potential disorder. This paper presents the requirements of the toy and discusses our choices in toy design, technology used, selected sensors, process to gather data from the sensors and generate information that will help in the decision-making and communication of the information to the collector system. In addition, we also describe the play activities the system supports. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Calorimetry Minisensor for the Localised Measurement of Surface Heat Dissipated from the Human Body
Sensors 2016, 16(11), 1864; https://doi.org/10.3390/s16111864
Received: 21 September 2016 / Revised: 28 October 2016 / Accepted: 3 November 2016 / Published: 6 November 2016
Cited by 3 | PDF Full-text (6241 KB) | HTML Full-text | XML Full-text
Abstract
We have developed a calorimetry sensor that can perform a local measurement of the surface heat dissipated from the human body. The operating principle is based on the law of conductive heat transfer: heat dissipated by the human body passes across a thermopile [...] Read more.
We have developed a calorimetry sensor that can perform a local measurement of the surface heat dissipated from the human body. The operating principle is based on the law of conductive heat transfer: heat dissipated by the human body passes across a thermopile located between the individual and a thermostat. Body heat power is calculated from the signals measured by the thermopile and the amount of power dissipated across the thermostat in order to maintain a constant temperature. The first prototype we built had a detection area measuring 6 × 6 cm2, while the second prototype, which is described herein, had a 2 × 2 cm2 detection area. This new design offers three advantages over the initial one: (1) greater resolution and three times greater thermal sensitivity; (2) a twice as fast response; and (3) it can take measurements from smaller areas of the body. The sensor has a 5 mW resolution, but the uncertainty is greater, up to 15 mW, due to the measurement and calculation procedure. The order of magnitude of measurements made in healthy subjects ranged from 60 to 300 mW at a thermostat temperature of 28 °C and an ambient room temperature of 21 °C. The values measured by the sensor depend on the ambient temperature and the thermostat’s temperature, while the power dissipated depends on the individual’s metabolism and any physical and/or emotional activity. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition
Sensors 2016, 16(11), 1807; https://doi.org/10.3390/s16111807
Received: 30 August 2016 / Revised: 24 October 2016 / Accepted: 25 October 2016 / Published: 28 October 2016
Cited by 19 | PDF Full-text (14141 KB) | HTML Full-text | XML Full-text
Abstract
In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people [...] Read more.
In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Vision-Based Approach for Building Telecare and Telerehabilitation Services
Sensors 2016, 16(10), 1724; https://doi.org/10.3390/s16101724
Received: 19 July 2016 / Revised: 23 September 2016 / Accepted: 9 October 2016 / Published: 18 October 2016
Cited by 7 | PDF Full-text (1201 KB) | HTML Full-text | XML Full-text
Abstract
In the last few years, telerehabilitation and telecare have become important topics in healthcare since they enable people to remain independent in their own homes by providing person-centered technologies to support the individual. These technologies allows elderly people to be assisted in their [...] Read more.
In the last few years, telerehabilitation and telecare have become important topics in healthcare since they enable people to remain independent in their own homes by providing person-centered technologies to support the individual. These technologies allows elderly people to be assisted in their home, instead of traveling to a clinic, providing them wellbeing and personalized health care. The literature shows a great number of interesting proposals to address telerehabilitation and telecare scenarios, which may be mainly categorized into two broad groups, namely wearable devices and context-aware systems. However, we believe that these apparently different scenarios may be addressed by a single context-aware approach, concretely a vision-based system that can operate automatically in a non-intrusive way for the elderly, and this is the goal of this paper. We present a general approach based on 3D cameras and neural network algorithms that offers an efficient solution for two different scenarios of telerehabilitation and telecare for elderly people. Our empirical analysis reveals the effectiveness and accuracy of the algorithms presented in our approach and provides more than promising results when the neural network parameters are properly adjusted. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
3D Printed Dry EEG Electrodes
Sensors 2016, 16(10), 1635; https://doi.org/10.3390/s16101635
Received: 26 July 2016 / Revised: 16 September 2016 / Accepted: 28 September 2016 / Published: 2 October 2016
Cited by 18 | PDF Full-text (2547 KB) | HTML Full-text | XML Full-text
Abstract
Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, [...] Read more.
Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface
Sensors 2016, 16(10), 1582; https://doi.org/10.3390/s16101582
Received: 20 July 2016 / Revised: 17 September 2016 / Accepted: 21 September 2016 / Published: 24 September 2016
Cited by 10 | PDF Full-text (2873 KB) | HTML Full-text | XML Full-text
Abstract
All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and [...] Read more.
All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression
Sensors 2016, 16(7), 1115; https://doi.org/10.3390/s16071115
Received: 14 March 2016 / Revised: 6 June 2016 / Accepted: 8 June 2016 / Published: 19 July 2016
Cited by 6 | PDF Full-text (1413 KB) | HTML Full-text | XML Full-text
Abstract
The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique [...] Read more.
The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Multi-Section Sensing and Vibrotactile Perception for Walking Guide of Visually Impaired Person
Sensors 2016, 16(7), 1070; https://doi.org/10.3390/s16071070
Received: 27 April 2016 / Revised: 24 June 2016 / Accepted: 6 July 2016 / Published: 12 July 2016
Cited by 8 | PDF Full-text (8991 KB) | HTML Full-text | XML Full-text
Abstract
Electronic Travel Aids (ETAs) improve the mobility of visually-impaired persons, but it is not easy to develop an ETA satisfying all the factors needed for reliable object detection, effective notification, and actual usability. In this study, the authors developed an easy-to-use ETA having [...] Read more.
Electronic Travel Aids (ETAs) improve the mobility of visually-impaired persons, but it is not easy to develop an ETA satisfying all the factors needed for reliable object detection, effective notification, and actual usability. In this study, the authors developed an easy-to-use ETA having the function of reliable object detection and its successful feedback to the user by tactile stimulation. Seven ultrasonic sensors facing in different directions detect obstacles in the walking path, while vibrators in the tactile display stimulate the hand according to the distribution of obstacles. The detection of ground drop-offs activates the electromagnetic brakes linked to the rear wheels. To verify the feasibility of the developed ETA in the outdoor environment, walking tests by blind participants were performed, and the evaluation of safety to ground drop-offs was carried out. From the experiment, the feasibility of the developed ETA was shown to be sufficient if the sensor ranges for hanging obstacle detection is improved and learning time is provided for the ETA. Finally, the light-weight and low cost ETA designed and assembled based on the evaluation of the developed ETA is introduced to show the improvement of portability and usability, and is compared with the previously developed ETAs. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Smart Sensing System for the Prognostic Monitoring of Bone Health
Sensors 2016, 16(7), 976; https://doi.org/10.3390/s16070976
Received: 8 May 2016 / Revised: 21 June 2016 / Accepted: 22 June 2016 / Published: 24 June 2016
Cited by 10 | PDF Full-text (3989 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this paper is to report a novel non-invasive, real-time, and label-free smart assay technique for the prognostic detection of bone loss by electrochemical impedance spectroscopy (EIS). The proposed system incorporated an antibody-antigen-based sensor functionalization to induce selectivity for the C-terminal [...] Read more.
The objective of this paper is to report a novel non-invasive, real-time, and label-free smart assay technique for the prognostic detection of bone loss by electrochemical impedance spectroscopy (EIS). The proposed system incorporated an antibody-antigen-based sensor functionalization to induce selectivity for the C-terminal telopeptide type one collagen (CTx-I) molecules—a bone loss biomarker. Streptavidin agarose was immobilized on the sensing area of a silicon substrate-based planar sensor, patterned with gold interdigital electrodes, to capture the antibody-antigen complex. Calibration experiments were conducted with various known CTx-I concentrations in a buffer solution to obtain a reference curve that was used to quantify the concentration of an analyte in the unknown serum samples. Multivariate chemometric analyses were done to determine the performance viability of the developed system. The analyses suggested that a frequency of 710 Hz is the most discriminating regarding the system sensitivity. A detection limit of 0.147 ng/mL was achieved for the proposed sensor and the corresponding reference curve was linear in the range of 0.147 ng/mL to 2.669 ng/mL. Two sheep blood samples were tested by the developed technique and the results were validated using enzyme-linked immunosorbent assay (ELISA). The results from the proposed technique match those from the ELISA. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing
Sensors 2016, 16(6), 942; https://doi.org/10.3390/s16060942
Received: 24 March 2016 / Revised: 11 June 2016 / Accepted: 16 June 2016 / Published: 22 June 2016
Cited by 2 | PDF Full-text (5173 KB) | HTML Full-text | XML Full-text
Abstract
In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output [...] Read more.
In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output frequency change of the culture bottle when the device is triggered. After the culture process is finished, a nitrite ion detecting solution based on a commercial nitrite ion detection kit is injected into the culture bottle by a syringe pump to check bacterial growth by the formation of a pigment by the reaction between the solution and the color sensor. Sensor status and NRA results are broadcasted via a Bluetooth low energy beacon. An Android application was developed to collect the broadcasted data, classify the status of cultured samples from multiple devices, and visualize the data for the end users, circumventing the need to examine each culture bottle manually during a long culture period. The authors expect that usage of the developed sensor will decrease the cost and required labor for handling large amounts of patient samples in local health centers in developing countries. All 3D-printerable hardware parts, a circuit diagram, and software are available online. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Bioimpedance Vector Analysis in Diagnosing Severe and Non-Severe Dengue Patients
Sensors 2016, 16(6), 911; https://doi.org/10.3390/s16060911
Received: 6 April 2016 / Revised: 17 May 2016 / Accepted: 18 May 2016 / Published: 18 June 2016
Cited by 1 | PDF Full-text (1787 KB) | HTML Full-text | XML Full-text
Abstract
Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected [...] Read more.
Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle
Abnormal Activity Detection Using Pyroelectric Infrared Sensors
Sensors 2016, 16(6), 822; https://doi.org/10.3390/s16060822
Received: 23 March 2016 / Revised: 30 May 2016 / Accepted: 31 May 2016 / Published: 3 June 2016
Cited by 8 | PDF Full-text (1131 KB) | HTML Full-text | XML Full-text
Abstract
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity [...] Read more.
Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Review

Jump to: Research

Open AccessReview
A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates
Sensors 2016, 16(12), 2134; https://doi.org/10.3390/s16122134
Received: 15 October 2016 / Revised: 8 December 2016 / Accepted: 9 December 2016 / Published: 14 December 2016
Cited by 16 | PDF Full-text (1649 KB) | HTML Full-text | XML Full-text
Abstract
Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization [...] Read more.
Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessReview
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions
Sensors 2016, 16(8), 1304; https://doi.org/10.3390/s16081304
Received: 5 April 2016 / Revised: 25 May 2016 / Accepted: 27 June 2016 / Published: 17 August 2016
Cited by 54 | PDF Full-text (959 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation [...] Read more.
In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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