E-Mail Alert

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

Journal Browser

Journal Browser

Special Issue "Wearable Biomedical Sensors"

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

Deadline for manuscript submissions: closed (31 October 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Steffen Leonhardt

Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, D-52074 Aachen, Germany
Website1 | Website2 | E-Mail
Fax: +49 241 80-623211
Interests: physiological measurement techniques; personal health care systems and feedback control systems in medicine
Guest Editor
Dr. Daniel Teichmann

Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, D-52074 Aachen, Germany
Website | E-Mail
Fax: +49 241 80 -623211
Interests: biomedical monitoring; signal processing and data analysis

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in a Special Issue on “Wearable Biomedical Sensors”. This Special Issue shall concentrate on wearable sensors for the monitoring of vital signs, motion, and other body-related information. Wearable biomedical sensor devices offer a variety of benefits. They do not restrict motion, offer flexibility, and may provide new degrees of freedom and information in completely new clinical but especially out-of-hospital settings. While application areas may be sports, domestic environments, and care facilities, wearable sensors can be of special benefit for the elderly and people suffering from disease.

Contributions to this Special Issue may include, but are not limited to: New wearable sensor devices, novel sensor principles particular suitable for wearable monitoring, low-power designs of already known sensor techniques, textile sensors; and the evaluation of wearable sensors during operation under realistic conditions.

Prof. Dr. Steffen Leonhardt
Dr. Daniel Teichmann
Guest Editors

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. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue 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).

Keywords

  • Non-contact
  • Mobile
  • Body sensor network
  • Vital signs
  • Textiles
  • Low power

Published Papers (26 papers)

View options order results:
result details:
Displaying articles 1-26
Export citation of selected articles as:

Research

Open AccessArticle Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network
Sensors 2017, 17(3), 478; doi:10.3390/s17030478
Received: 7 December 2016 / Revised: 17 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
PDF Full-text (10155 KB) | HTML Full-text | XML Full-text
Abstract
The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for
[...] Read more.
The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording
Sensors 2017, 17(3), 457; doi:10.3390/s17030457
Received: 1 October 2016 / Revised: 16 January 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
PDF Full-text (2225 KB) | HTML Full-text | XML Full-text
Abstract
Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain.
[...] Read more.
Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle A Novel Earphone Type Sensor for Measuring Mealtime: Consideration of the Method to Distinguish between Running and Meals
Sensors 2017, 17(2), 252; doi:10.3390/s17020252
Received: 25 October 2016 / Accepted: 24 January 2017 / Published: 27 January 2017
PDF Full-text (4534 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we describe a technique for estimating meal times using an earphone-type wearable sensor. A small optical sensor composed of a light-emitting diode and phototransistor is inserted into the ear hole of a user and estimates the meal times of the
[...] Read more.
In this study, we describe a technique for estimating meal times using an earphone-type wearable sensor. A small optical sensor composed of a light-emitting diode and phototransistor is inserted into the ear hole of a user and estimates the meal times of the user from the time variations in the amount of light received. This is achieved by emitting light toward the inside of the ear canal and receiving light reflected back from the ear canal. This proposed technique allowed “meals” to be differentiated from having conversations, sneezing, walking, ascending and descending stairs, operating a computer, and using a smartphone. Conventional devices worn on the head of users and that measure food intake can vibrate during running as the body is jolted more violently than during walking; this can result in the misidentification of running as eating by these devices. To solve this problem, we used two of our sensors simultaneously: one in the left ear and one in the right ear. This was based on our finding that measurements from the left and right ear canals have a strong correlation during running but no correlation during eating. This allows running and eating to be distinguished based on correlation coefficients, which can reduce misidentification. Moreover, by using an optical sensor composed of a semiconductor, a small and lightweight device can be created. This measurement technique can also measure body motion associated with running, and the data obtained from the optical sensor inserted into the ear can be used to support a healthy lifestyle regarding both eating and exercise. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Quantification of Finger-Tapping Angle Based on Wearable Sensors
Sensors 2017, 17(2), 203; doi:10.3390/s17020203
Received: 23 October 2016 / Revised: 15 January 2017 / Accepted: 16 January 2017 / Published: 25 January 2017
PDF Full-text (1741 KB) | HTML Full-text | XML Full-text
Abstract
We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a
[...] Read more.
We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle SisFall: A Fall and Movement Dataset
Sensors 2017, 17(1), 198; doi:10.3390/s17010198
Received: 22 October 2016 / Revised: 24 December 2016 / Accepted: 3 January 2017 / Published: 20 January 2017
PDF Full-text (694 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and
[...] Read more.
Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection
Sensors 2017, 17(1), 171; doi:10.3390/s17010171
Received: 13 November 2016 / Revised: 27 December 2016 / Accepted: 13 January 2017 / Published: 17 January 2017
Cited by 1 | PDF Full-text (3373 KB) | HTML Full-text | XML Full-text
Abstract
In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several
[...] Read more.
In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors
Sensors 2017, 17(1), 158; doi:10.3390/s17010158
Received: 9 November 2016 / Revised: 7 January 2017 / Accepted: 9 January 2017 / Published: 14 January 2017
PDF Full-text (1970 KB) | HTML Full-text | XML Full-text
Abstract
Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals
[...] Read more.
Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach
Sensors 2017, 17(1), 112; doi:10.3390/s17010112
Received: 5 September 2016 / Revised: 19 December 2016 / Accepted: 20 December 2016 / Published: 8 January 2017
PDF Full-text (4955 KB) | HTML Full-text | XML Full-text
Abstract
Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk
[...] Read more.
Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals
Sensors 2017, 17(1), 9; doi:10.3390/s17010009
Received: 19 October 2016 / Revised: 12 December 2016 / Accepted: 14 December 2016 / Published: 22 December 2016
PDF Full-text (483 KB) | HTML Full-text | XML Full-text
Abstract
This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality,
[...] Read more.
This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model
Sensors 2016, 16(12), 2172; doi:10.3390/s16122172
Received: 30 October 2016 / Revised: 10 December 2016 / Accepted: 13 December 2016 / Published: 17 December 2016
Cited by 1 | PDF Full-text (1880 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people’s postures and activities to facilitate healthy aging. In particular, such
[...] Read more.
Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people’s postures and activities to facilitate healthy aging. In particular, such devices should be accepted by elderly people so that they are willing to wear it all the time. This paper presents the design and development of a novel, textile-based, intelligent wearable vest for real-time posture monitoring and emergency warnings. The vest provides a highly portable and low-cost solution that can be used both indoors and outdoors in order to provide long-term care at home, including health promotion, healthy aging assessments, and health abnormality alerts. The usability of the system was verified using a technology acceptance model-based study of 50 elderly people. The results indicated that although elderly people are anxious about some newly developed wearable technologies, they look forward to wearing this instrumented posture-monitoring vest in the future. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases
Sensors 2016, 16(12), 2151; doi:10.3390/s16122151
Received: 31 October 2016 / Revised: 5 December 2016 / Accepted: 12 December 2016 / Published: 16 December 2016
Cited by 1 | PDF Full-text (2769 KB) | HTML Full-text | XML Full-text
Abstract
One of the important aspects to be considered in rheumatic and musculoskeletal diseases is the patient’s activity capacity (or performance), defined as the ability to perform a task. Currently, it is assessed by physicians or health professionals mainly by means of a patient-reported
[...] Read more.
One of the important aspects to be considered in rheumatic and musculoskeletal diseases is the patient’s activity capacity (or performance), defined as the ability to perform a task. Currently, it is assessed by physicians or health professionals mainly by means of a patient-reported questionnaire, sometimes combined with the therapist’s judgment on performance-based tasks. This work introduces an approach to assess the activity capacity at home in a more objective, yet interpretable way. It offers a pilot study on 28 patients suffering from axial spondyloarthritis (axSpA) to demonstrate its efficacy. Firstly, a protocol is introduced to recognize a limited set of six transition activities in the home environment using a single accelerometer. To this end, a hierarchical classifier with the rejection of non-informative activity segments has been developed drawing on both direct pattern recognition and statistical signal features. Secondly, the recognized activities should be assessed, similarly to the scoring performed by patients themselves. This is achieved through the interval coded scoring (ICS) system, a novel method to extract an interpretable scoring system from data. The activity recognition reaches an average accuracy of 93.5%; assessment is currently 64.3% accurate. These results indicate the potential of the approach; a next step should be its validation in a larger patient study. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control
Sensors 2016, 16(12), 2050; doi:10.3390/s16122050
Received: 29 August 2016 / Revised: 22 October 2016 / Accepted: 8 November 2016 / Published: 2 December 2016
PDF Full-text (7230 KB) | HTML Full-text | XML Full-text
Abstract
To recognize the user’s motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control
[...] Read more.
To recognize the user’s motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration
Sensors 2016, 16(11), 1779; doi:10.3390/s16111779
Received: 22 July 2016 / Revised: 28 September 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
PDF Full-text (3349 KB) | HTML Full-text | XML Full-text
Abstract
This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in
[...] Read more.
This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in breathing air condense gradually on the surface of the sensor so as to form a thin water film that leads to a significant sensor drift in long-duration respiratory monitoring. The water film is formed by a combination of condensation and evaporation, and therefore the behavior of the humidity drift is complicated. Fortunately, the exhale and inhale responses of the sensor exhibit distinguishing features that are different from the humidity drift. Using a wavelet analysis method, we removed the baseline drift of the sensor and successfully recovered the respiratory waveform. Finally, we extracted apnea-hypopnea events from the respiratory signals monitored in whole-night sleeps of patients and compared them with golden standard polysomnography (PSG) results. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Open AccessArticle Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System
Sensors 2016, 16(10), 1744; doi:10.3390/s16101744
Received: 30 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
Cited by 1 | PDF Full-text (2333 KB) | HTML Full-text | XML Full-text
Abstract
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove
[...] Read more.
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation
Sensors 2016, 16(10), 1739; doi:10.3390/s16101739
Received: 2 June 2016 / Revised: 20 August 2016 / Accepted: 21 September 2016 / Published: 19 October 2016
PDF Full-text (4190 KB) | HTML Full-text | XML Full-text
Abstract
Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used
[...] Read more.
Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors
Sensors 2016, 16(10), 1579; doi:10.3390/s16101579
Received: 28 June 2016 / Revised: 18 September 2016 / Accepted: 20 September 2016 / Published: 27 September 2016
Cited by 1 | PDF Full-text (4612 KB) | HTML Full-text | XML Full-text
Abstract
Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint
[...] Read more.
Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle A Self-Powered Insole for Human Motion Recognition
Sensors 2016, 16(9), 1502; doi:10.3390/s16091502
Received: 11 July 2016 / Revised: 20 August 2016 / Accepted: 9 September 2016 / Published: 15 September 2016
PDF Full-text (3998 KB) | HTML Full-text | XML Full-text
Abstract
Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human
[...] Read more.
Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human motion recognition sensor is reported. The insole is designed as a sandwich structure consisting of two wavy silica gel film separated by a flexible piezoelectric foil stave, which has higher performance compared with conventional piezoelectric harvesters with cantilever structure. The energy harvesting insole is capable of driving some common electronics by scavenging energy from human walking. Moreover, it can be used to recognize human motion as the waveforms it generates change when people are in different locomotion modes. It is demonstrated that different types of human motion such as walking and running are clearly classified by the insole without any external power source. This work not only expands the applications of piezoelectric energy harvesters for wearable power supplies and self-powered sensors, but also provides possible approaches for wearable self-powered human motion monitoring that is of great importance in many fields such as rehabilitation and sports science. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice
Sensors 2016, 16(8), 1161; doi:10.3390/s16081161
Received: 29 May 2016 / Revised: 3 July 2016 / Accepted: 20 July 2016 / Published: 25 July 2016
Cited by 4 | PDF Full-text (2208 KB) | HTML Full-text | XML Full-text
Abstract
Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly
[...] Read more.
Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Open AccessArticle Test-Retest Reliability of an Automated Infrared-Assisted Trunk Accelerometer-Based Gait Analysis System
Sensors 2016, 16(8), 1156; doi:10.3390/s16081156
Received: 19 May 2016 / Revised: 16 July 2016 / Accepted: 21 July 2016 / Published: 23 July 2016
PDF Full-text (2143 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to determine the test-retest reliability of an automated infrared-assisted, trunk accelerometer-based gait analysis system for measuring gait parameters of healthy subjects in a hospital. Thirty-five participants (28 of them females; age range, 23–79 years) performed a 5-m
[...] Read more.
The aim of this study was to determine the test-retest reliability of an automated infrared-assisted, trunk accelerometer-based gait analysis system for measuring gait parameters of healthy subjects in a hospital. Thirty-five participants (28 of them females; age range, 23–79 years) performed a 5-m walk twice using an accelerometer-based gait analysis system with infrared assist. Measurements of spatiotemporal gait parameters (walking speed, step length, and cadence) and trunk control (gait symmetry, gait regularity, acceleration root mean square (RMS), and acceleration root mean square ratio (RMSR)) were recorded in two separate walking tests conducted 1 week apart. Relative and absolute test-retest reliability was determined by calculating the intra-class correlation coefficient (ICC3,1) and smallest detectable difference (SDD), respectively. The test-retest reliability was excellent for walking speed (ICC = 0.87, 95% confidence interval = 0.74–0.93, SDD = 13.4%), step length (ICC = 0.81, 95% confidence interval = 0.63–0.91, SDD = 12.2%), cadence (ICC = 0.81, 95% confidence interval = 0.63–0.91, SDD = 10.8%), and trunk control (step and stride regularity in anterior-posterior direction, acceleration RMS and acceleration RMSR in medial-lateral direction, and acceleration RMS and stride regularity in vertical direction). An automated infrared-assisted, trunk accelerometer-based gait analysis system is a reliable tool for measuring gait parameters in the hospital environment. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Figures

Figure 1

Open AccessArticle The Evaluation of Physical Stillness with Wearable Chest and Arm Accelerometer during Chan Ding Practice
Sensors 2016, 16(7), 1126; doi:10.3390/s16071126
Received: 11 April 2016 / Revised: 18 June 2016 / Accepted: 4 July 2016 / Published: 20 July 2016
Cited by 1 | PDF Full-text (1318 KB) | HTML Full-text | XML Full-text
Abstract
Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to
[...] Read more.
Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to maintain physical stillness. In this article, wireless wearable accelerometers were used to detect physical stillness, and the created physical stillness index (PSI) was also shown. Ninety college students participated in this study. Primarily, accelerometers used on the arms and chest were examined. The results showed that the PSI values on the arms were higher than that of the chest, when participants moved their bodies in three different ways, left-right, anterior-posterior, and hand, movements with natural breathing. Then, they were divided into three groups to practice Chan Ding for approximately thirty minutes. Participants without any Chan Ding experience were in Group I. Participants with one year of Chan Ding experience were in Group II, and participants with over three year of experience were in Group III. The Chinese Happiness Inventory (CHI) was also conducted. Results showed that the PSI of the three groups measured during 20–30 min were 0.123 ± 0.155, 0.012 ± 0.013, and 0.001 ± 0.0003, respectively (p < 0.001 ***). The averaged CHI scores of the three groups were 10.13, 17.17, and 25.53, respectively (p < 0.001 ***). Correlation coefficients between PSI and CHI of the three groups were −0.440, −0.369, and −0.537, respectively (p < 0.01 **). PSI value and the wearable accelerometer that are presently available on the market could be used to evaluate the quality of the physical stillness of the participants during Chan Ding practice. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle A Novel Wearable Device for Food Intake and Physical Activity Recognition
Sensors 2016, 16(7), 1067; doi:10.3390/s16071067
Received: 26 May 2016 / Revised: 7 July 2016 / Accepted: 8 July 2016 / Published: 11 July 2016
Cited by 7 | PDF Full-text (1364 KB) | HTML Full-text | XML Full-text
Abstract
Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active
[...] Read more.
Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor
Sensors 2016, 16(5), 750; doi:10.3390/s16050750
Received: 10 March 2016 / Revised: 10 May 2016 / Accepted: 17 May 2016 / Published: 23 May 2016
Cited by 1 | PDF Full-text (5125 KB) | HTML Full-text | XML Full-text
Abstract
Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches
[...] Read more.
Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches are costly and often require overnight treatment in clinics. With this in mind, the research presented here has emerged from the question: “Can data be easily collected and analyzed without causing discomfort to patients?” Therefore, the aim of this study is to provide a novel monitoring system for quantifying sleep quality. The data acquisition system is equipped with multimodal sensors, including a three-axis accelerometer and a pressure sensor. To identify sleep quality based on measured data, a novel algorithm, which uses numerous physiological parameters, was proposed. Such parameters include non-REM sleep time, the number of apneic episodes, and sleep durations for dominant poses. To assess the effectiveness of the proposed system, three participants were enrolled in this experimental study for a duration of 20 days. From the experimental results, it can be seen that the proposed monitoring system is effective for quantifying sleep quality. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors
Sensors 2016, 16(5), 715; doi:10.3390/s16050715
Received: 18 February 2016 / Revised: 5 May 2016 / Accepted: 11 May 2016 / Published: 17 May 2016
Cited by 1 | PDF Full-text (7210 KB) | HTML Full-text | XML Full-text
Abstract
Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R
[...] Read more.
Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography
Sensors 2016, 16(5), 646; doi:10.3390/s16050646
Received: 16 February 2016 / Revised: 24 April 2016 / Accepted: 30 April 2016 / Published: 5 May 2016
Cited by 3 | PDF Full-text (1326 KB) | HTML Full-text | XML Full-text
Abstract
Polysomnography (PSG) is the “gold standard” for monitoring sleep. Alternatives to PSG are of interest for clinical, research, and personal use. Wrist-worn actigraph devices have been utilized in research settings for measures of sleep for over two decades. Whether sleep measures from commercially
[...] Read more.
Polysomnography (PSG) is the “gold standard” for monitoring sleep. Alternatives to PSG are of interest for clinical, research, and personal use. Wrist-worn actigraph devices have been utilized in research settings for measures of sleep for over two decades. Whether sleep measures from commercially available devices are similarly valid is unknown. We sought to determine the validity of five wearable devices: Basis Health Tracker, Misfit Shine, Fitbit Flex, Withings Pulse O2, and a research-based actigraph, Actiwatch Spectrum. We used Wilcoxon Signed Rank tests to assess differences between devices relative to PSG and correlational analysis to assess the strength of the relationship. Data loss was greatest for Fitbit and Misfit. For all devices, we found no difference and strong correlation of total sleep time with PSG. Sleep efficiency differed from PSG for Withings, Misfit, Fitbit, and Basis, while Actiwatch mean values did not differ from that of PSG. Only mean values of sleep efficiency (time asleep/time in bed) from Actiwatch correlated with PSG, yet this correlation was weak. Light sleep time differed from PSG (nREM1 + nREM2) for all devices. Measures of Deep sleep time did not differ from PSG (SWS + REM) for Basis. These results reveal the current strengths and limitations in sleep estimates produced by personal health monitoring devices and point to a need for future development. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness
Sensors 2016, 16(4), 592; doi:10.3390/s16040592
Received: 3 March 2016 / Revised: 11 April 2016 / Accepted: 17 April 2016 / Published: 23 April 2016
PDF Full-text (1983 KB) | HTML Full-text | XML Full-text
Abstract
This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on
[...] Read more.
This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Open AccessArticle Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting
Sensors 2016, 16(4), 524; doi:10.3390/s16040524
Received: 8 March 2016 / Revised: 1 April 2016 / Accepted: 6 April 2016 / Published: 12 April 2016
Cited by 3 | PDF Full-text (2724 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of
[...] Read more.
In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities—walking, walking up and down stairs, jogging and running—were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW–74 µW using both transducers for each joint. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Portable Real-Time Remote Multiple ECG Signals Monitoring System for Various Arrhythmias Detection and Warning
Author: Shing-Tai Pan
Abstract: In this paper, a portable system for arrhythmia monitoring with mutiple users is implemented. A wearable ECG sensor is used to record ECG signals. Based on these ECG signals, this system can automatically recognize various arrhythmias and alarms if necessary. To precisely estimate the arrhythmias, the key point is to find better features of ECG for each arrhythmia and to find the most efficient classifier. This paper will use adaptive features of ECG and apply Hidden Markov Model (HMM) to arrhythmia classification for getting a precise recognition result. The implemented system can simultaneously recognize various patients’ heartbeats timely at sleep or at daylight both indoor and outdoor. This system can also alarm the patients themselves, their families and some medical personnel when the emergent case occurs.

Title: An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education.
Author: Juan Lemos
Abstract: The introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee's performance. For this reason, it is necessary to develop automatic mechanisms to assess manual dexterity in a quantitative way. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. It has been designed to be used with a neurosurgical simulator, but can be adapted to benchtop- and manikin-based simulators. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. The system was validated with a sample of 14 volunteers who performed a test that was designed to evaluate their manual dexterity and the IGlove’s functionalities.

Title: A Novel Earphone Type Sensor for Measuring Mealtime-Consideration of the Method to Distinguish Between Running and Meal
Authors: Kazuhiro Taniguchi, Hikaru Chiaki, Mami Kurosawa and Atsushi Nishikawa
Abstract: In this study, we described a technique for estimating mealtimes using an earphone-type wearable sensor. A small optical sensor composed of a light-emitting diode and phototransistor is inserted into the ear hole of a user and estimates the mealtimes of the user from the time variations in the amount flight received. This is achieved by emitting light toward the inside of the ear canal and receiving light reflected back from the ear canal. Conventional devices worn on the head of users and that measure food intake can vibrate during running as the body is jolted more violently than during walking; this can result in the misidentification of running as eating by these devices. However, using our proposed method allows for the differentiation between running and eating, which can reduce misidentification. Moreover, using an optical sensor composed of a semiconductor, a small and lightweight device can be created. This measurement technique can also measure body motion associated with running, and the data obtained from the optical sensor inserted into the ear can be used to support a healthy lifestyle regarding both eating and exercise.

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Sensors Edit a special issue Review for Sensors
Back to Top