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Special Issue "Wearable Sensors and Devices for Healthcare Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 15 June 2019

Special Issue Editors

Guest Editor
Dr. Wee Ser

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Website | E-Mail
Interests: biomedical signal processing and machine learning for physiological signal classifications; wearable and portable healthcare monitoring devices
Guest Editor
Dr. Nan Liu

Duke-NUS Medical School, National University of Singapore, Singapore
Website | E-Mail
Interests: wearable medical devices, health services research, cardiology, emergency and critical care, health informatics and medical innovation

Special Issue Information

Dear Colleagues,

With advances in signal processing, material science, electronic design, and computing power, numerous wearable sensors and devices have been developed over the past few decades. In healthcare, wearable technologies are witnessing increasing needs and interest. The volumes of data generated from wearable devices could be useful in identifying health risks. Potential applications of wearable sensors and devices are broad and have promising impacts on patient care. While traditional circuits and systems are the main focus of development, artificial intelligence and smart technologies continuously open up possibilities in this area. Intelligent solutions are essentially important in post-sensing data processing, information fusion, and decision making. This Special Issue aims to report the latest scholarly technological updates in wearable sensors and devices, and their applications in healthcare. Possible topics include, but are not limited to:

  • Wearable sensors, devices, or techniques for physiological monitoring
  • Wearable sensors, devices, or techniques for medical decision making
  • Wearable sensors, devices, or techniques for web-based and mobile applications
  • Wearable sensors, devices, or techniques for telemedicine applications
  • Wearable sensors, devices, or techniques for activities modelling
  • Wearable sensors, devices, or techniques for body sensor networks
  • Circuits and systems for wearable sensors, devices, or techniques
  • Communications systems for wearable sensors and devices
  • Intelligent and expert systems for wearable sensors, devices, or techniques
  • Information fusion for wearable sensors, devices, or techniques
  • Health data privacy in wearable sensors, devices, or techniques

Dr. Wee Ser
Dr. Nan Liu
Guest Editors

Manuscript Submission Information

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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

  • wearable sensors
  • wearable devices
  • wearable techniques
  • healthcare

Published Papers (11 papers)

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Research

Open AccessArticle
Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths
Sensors 2019, 19(8), 1925; https://doi.org/10.3390/s19081925
Received: 22 March 2019 / Revised: 17 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
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Abstract
Assessing interventions for mobility disorders using real-life movement remains an unsolved problem. We propose a new method combining the strengths of traditional laboratory studies where environment is strictly controlled, and field-based studies where subjects behave naturally. We use a foot-mounted inertial sensor, a [...] Read more.
Assessing interventions for mobility disorders using real-life movement remains an unsolved problem. We propose a new method combining the strengths of traditional laboratory studies where environment is strictly controlled, and field-based studies where subjects behave naturally. We use a foot-mounted inertial sensor, a GPS receiver and a barometric altitude sensor to reconstruct a subject’s path and detailed foot movement, both indoors and outdoors, during days-long measurement using strapdown navigation and sensor fusion algorithms. We cluster repeated movement paths based on location, and propose that on these paths, most environmental and behavioral factors (e.g., terrain and motivation) are as repeatable as in a laboratory. During each bout of movement along a frequently repeated path, any synchronized measurement can be isolated for study, enabling focused statistical comparison of different interventions. We conducted a 10-day test on one subject wearing athletic shoes and sandals each for five days. The algorithm detected four frequently-repeated straight walking paths with at least 300 total steps and repetitions on at least three days for each condition. Results on these frequently-repeated paths indicated significantly lower foot clearance and shorter stride length and a trend toward decreased stride width when wearing athletic shoes vs. sandals. Comparisons based on all straight walking were similar, showing greater statistical power, but higher variability in the data. The proposed method offers a new way to evaluate how mobility interventions affect everyday movement behavior. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
A Wearable Combined Wrist Pulse Measurement System Using Airbags for Pressurization
Sensors 2019, 19(2), 386; https://doi.org/10.3390/s19020386
Received: 13 December 2018 / Revised: 9 January 2019 / Accepted: 15 January 2019 / Published: 18 January 2019
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Abstract
The pulse measurement instrument is based on traditional Chinese medicine (TCM) and is used to collect the pulse of patients to assist in diagnosis and treatment. In the existing pulse measurement system, desktop devices have large volumes, complex pressure adjusting operations, and unstable [...] Read more.
The pulse measurement instrument is based on traditional Chinese medicine (TCM) and is used to collect the pulse of patients to assist in diagnosis and treatment. In the existing pulse measurement system, desktop devices have large volumes, complex pressure adjusting operations, and unstable pressurization. Wearable devices tend to have no pressurization function or the function to pressurize three channels separately, which are not consistent with the diagnostic method in TCM. This study constructs a wearable pulse measurement system using airbags for pressurization. This system uses guide plates, guide grooves, and positioning screws to adjust the relative position of the wristband and locate Cun, Guan and Chi regions. The pulse signal measured by the sensor is collected and sent to a computer by microcontroller unit. In experiments, this system successfully obtains the best pulse-taking pressure, its pulse waveform under continuous decompression, and the pulse waveform of three regions under light, medium, and heavy pressure. Compared with the existing technology, the system has the advantages of supporting single-region and three-region pulse acquisition, independent pressure adjustment, and position adjustment. It meets the needs of home, medical, and experimental research, and it is convenient and comfortable to wear and easy to carry. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Automatic Extraction and Detection of Characteristic Movement Patterns in Children with ADHD Based on a Convolutional Neural Network (CNN) and Acceleration Images
Sensors 2018, 18(11), 3924; https://doi.org/10.3390/s18113924
Received: 3 October 2018 / Revised: 26 October 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
PDF Full-text (1156 KB) | HTML Full-text | XML Full-text
Abstract
Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two [...] Read more.
Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental disorder, which is characterized by inattention, hyperactivity and impulsive behaviors. In particular, children have difficulty keeping still exhibiting increased fine and gross motor activity. This paper focuses on analyzing the data obtained from two tri-axial accelerometers (one on the wrist of the dominant arm and the other on the ankle of the dominant leg) worn during school hours by a group of 22 children (11 children with ADHD and 11 paired controls). Five of the 11 ADHD diagnosed children were not on medication during the study. The children were not explicitly instructed to perform any particular activity but followed a normal session at school alternating classes of little or moderate physical activity with intermediate breaks of more prominent physical activity. The tri-axial acceleration signals were converted into 2D acceleration images and a Convolutional Neural Network (CNN) was trained to recognize the differences between non-medicated ADHD children and their paired controls. The results show that there were statistically significant differences in the way the two groups moved for the wrist accelerometer (t-test p-value <0.05). For the ankle accelerometer statistical significance was only achieved between data from the non-medicated children in the experimental group and the control group. Using a Convolutional Neural Network (CNN) to automatically extract embedded acceleration patterns and provide an objective measure to help in the diagnosis of ADHD, an accuracy of 0.875 for the wrist sensor and an accuracy of 0.9375 for the ankle sensor was achieved. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Comparative Study on Conductive Knitted Fabric Electrodes for Long-Term Electrocardiography Monitoring: Silver-Plated and PEDOT:PSS Coated Fabrics
Sensors 2018, 18(11), 3890; https://doi.org/10.3390/s18113890
Received: 16 October 2018 / Revised: 5 November 2018 / Accepted: 10 November 2018 / Published: 12 November 2018
Cited by 1 | PDF Full-text (13180 KB) | HTML Full-text | XML Full-text
Abstract
Long-term monitoring of the electrical activity of the heart helps to detect the presence of potential dysfunctions, enabling the diagnosis of a wide range of cardiac pathologies. However, standard electrodes used for electrocardiogram (ECG) acquisition are not fully integrated into garments, and generally [...] Read more.
Long-term monitoring of the electrical activity of the heart helps to detect the presence of potential dysfunctions, enabling the diagnosis of a wide range of cardiac pathologies. However, standard electrodes used for electrocardiogram (ECG) acquisition are not fully integrated into garments, and generally need to be used with a gel to improve contact resistance. This article is focused on the development of washable screen-printed cotton, with and without Lycra, textile electrodes providing a medical quality ECG signal to be used for long-term electrocardiography measurements. Several samples with different Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) concentrations were investigated. Silver-plated knitted fabric electrodes were also used for comparison, within the same process of ECG signal recording. The acquisition of ECG signals carried out by a portable medical device and a low-coast Arduino-based device on one female subject in a sitting position. Three textile electrodes were placed on the right and left forearms and a ground electrode was placed on the right ankle of a healthy female subject. Plastic clamps were applied to maintain electrodes on the skin. The results obtained with PEDOT:PSS used for electrodes fabrication have been presented, considering the optimal concentration required for medical ECG quality and capacity to sustain up to 50 washing cycles. All the ECG signals acquired and recorded, using PEDOT:PSS and silver-plated electrodes, have been reviewed by a cardiologist in order to validate their quality required for accurate diagnosis. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis
Sensors 2018, 18(11), 3870; https://doi.org/10.3390/s18113870
Received: 7 October 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 10 November 2018
PDF Full-text (613 KB) | HTML Full-text | XML Full-text
Abstract
The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health [...] Read more.
The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Multi-Functional Soft Strain Sensors for Wearable Physiological Monitoring
Sensors 2018, 18(11), 3822; https://doi.org/10.3390/s18113822
Received: 22 September 2018 / Revised: 26 October 2018 / Accepted: 31 October 2018 / Published: 8 November 2018
Cited by 1 | PDF Full-text (1037 KB) | HTML Full-text | XML Full-text
Abstract
Wearable devices which monitor physiological measurements are of significant research interest for a wide number of applications including medicine, entertainment, and wellness monitoring. However, many wearable sensing systems are highly rigid and thus restrict the movement of the wearer, and are not modular [...] Read more.
Wearable devices which monitor physiological measurements are of significant research interest for a wide number of applications including medicine, entertainment, and wellness monitoring. However, many wearable sensing systems are highly rigid and thus restrict the movement of the wearer, and are not modular or customizable for a specific application. Typically, one sensor is designed to model one physiological indicator which is not a scalable approach. This work aims to address these limitations, by developing soft sensors and including conductive particles into a silicone matrix which allows sheets of soft strain sensors to be developed rapidly using a rapid manufacturing process. By varying the morphology of the sensor sheets and electrode placement the response can be varied. To demonstrate the versatility and range of sensitivity of this base sensing material, two wearable sensors have been developed which show the detection of different physiological parameters. These include a pressure-sensitive insole sensor which can detect ground reaction forces and a strain sensor which can be worn over clothes to allow the measurements of heart rate, breathing rate, and gait. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
A Mobile Cough Strength Evaluation Device Using Cough Sounds
Sensors 2018, 18(11), 3810; https://doi.org/10.3390/s18113810
Received: 11 October 2018 / Revised: 31 October 2018 / Accepted: 5 November 2018 / Published: 7 November 2018
PDF Full-text (4405 KB) | HTML Full-text | XML Full-text
Abstract
Although cough peak flow (CPF) is an important measurement for evaluating the risk of cough dysfunction, some patients cannot use conventional measurement instruments, such as spirometers, because of the configurational burden of the instruments. Therefore, we previously developed a cough strength estimation method [...] Read more.
Although cough peak flow (CPF) is an important measurement for evaluating the risk of cough dysfunction, some patients cannot use conventional measurement instruments, such as spirometers, because of the configurational burden of the instruments. Therefore, we previously developed a cough strength estimation method using cough sounds based on a simple acoustic and aerodynamic model. However, the previous model did not consider age or have a user interface for practical application. This study clarifies the cough strength prediction accuracy using an improved model in young and elderly participants. Additionally, a user interface for mobile devices was developed to record cough sounds and estimate cough strength using the proposed method. We then performed experiments on 33 young participants (21.3 ± 0.4 years) and 25 elderly participants (80.4 ± 6.1 years) to test the effect of age on the CPF estimation accuracy. The percentage error between the measured and estimated CPFs was approximately 6.19%. In addition, among the elderly participants, the current model improved the estimation accuracy of the previous model by a percentage error of approximately 6.5% (p < 0.001). Furthermore, Bland-Altman analysis demonstrated no systematic error between the measured and estimated CPFs. These results suggest that the developed device can be applied for daily CPF measurements in clinical practice. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Using the Pulse Contour Method to Measure the Changes in Stroke Volume during a Passive Leg Raising Test
Sensors 2018, 18(10), 3420; https://doi.org/10.3390/s18103420
Received: 5 September 2018 / Revised: 4 October 2018 / Accepted: 11 October 2018 / Published: 12 October 2018
PDF Full-text (1297 KB) | HTML Full-text | XML Full-text
Abstract
The pulse contour method is often used with the Windkessel model to measure stroke volume. We used a digital pressure and flow sensors to detect the parameters of the Windkessel model from the pulse waveform. The objective of this study was to assess [...] Read more.
The pulse contour method is often used with the Windkessel model to measure stroke volume. We used a digital pressure and flow sensors to detect the parameters of the Windkessel model from the pulse waveform. The objective of this study was to assess the stability and accuracy of this method by making use of the passive leg raising test. We studied 24 healthy subjects (40 ± 9.3 years), and used the Medis® CS 1000, an impedance cardiography, as the comparing reference. The pulse contour method measured the waveform of the brachial artery by using a cuff. The compliance and resistance of the peripheral artery was detected from the cuff characteristics and the blood pressure waveform. Then, according to the method proposed by Romano et al., the stroke volume could be measured. This method was implemented in our designed blood pressure monitor. A passive leg raising test, which could immediately change the preloading of the heart, was done to certify the performance of our method. The pulse contour method and impedance cardiography simultaneously measured the stroke volume. The measurement of the changes in stroke volume using the pulse contour method had a very high correlation with the Medis® CS 1000 measurement, the correlation coefficient of the changed ratio and changed differences in stroke volume were r2 = 0.712 and r2 = 0.709, respectively. It was shown that the stroke volume measured by using the pulse contour method was not accurate enough. But, the changes in the stroke volume could be accurately measured with this pulse contour method. Changes in stroke volume are often used to understand the conditions of cardiac preloading in the clinical field. Moreover, the operation of the pulse contour method is easier than using impedance cardiography and echocardiography. Thus, this method is suitable to use in different healthcare fields. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
An All-Organic Flexible Visible Light Communication System
Sensors 2018, 18(9), 3045; https://doi.org/10.3390/s18093045
Received: 26 July 2018 / Revised: 31 August 2018 / Accepted: 11 September 2018 / Published: 12 September 2018
Cited by 1 | PDF Full-text (3582 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Visible light communication systems can be used in a wide variety of applications, from driving to home automation. The use of wearables can increase the potential applications in indoor systems to send and receive specific and customized information. We have designed and developed [...] Read more.
Visible light communication systems can be used in a wide variety of applications, from driving to home automation. The use of wearables can increase the potential applications in indoor systems to send and receive specific and customized information. We have designed and developed a fully organic and flexible Visible Light Communication system using a flexible OLED, a flexible P3HT:PCBM-based organic photodiode (OPD) and flexible PCBs for the emitter and receiver conditioning circuits. We have fabricated and characterized the I-V curve, modulation response and impedance of the flexible OPD. As emitter we have used a commercial flexible organic luminaire with dimensions 99 × 99 × 0.88 mm, and we have characterized its modulation response. All the devices show frequency responses that allow operation over 40 kHz, thus enabling the transmission of high quality audio. Finally, we integrated the emitter and receiver components and its electronic drivers, to build an all-organic flexible VLC system capable of transmitting an audio file in real-time, as a proof of concept of the indoor capabilities of such a system. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network
Sensors 2018, 18(9), 2892; https://doi.org/10.3390/s18092892
Received: 16 July 2018 / Revised: 10 August 2018 / Accepted: 27 August 2018 / Published: 31 August 2018
Cited by 4 | PDF Full-text (3767 KB) | HTML Full-text | XML Full-text
Abstract
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful [...] Read more.
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Estimation of Cough Peak Flow Using Cough Sounds
Sensors 2018, 18(7), 2381; https://doi.org/10.3390/s18072381
Received: 8 June 2018 / Revised: 8 July 2018 / Accepted: 18 July 2018 / Published: 22 July 2018
Cited by 1 | PDF Full-text (3080 KB) | HTML Full-text | XML Full-text
Abstract
Cough peak flow (CPF) is a measurement for evaluating the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on [...] Read more.
Cough peak flow (CPF) is a measurement for evaluating the risk of cough dysfunction and can be measured using various devices, such as spirometers. However, complex device setup and the face mask required to be firmly attached to the mouth impose burdens on both patients and their caregivers. Therefore, this study develops a novel cough strength evaluation method using cough sounds. This paper presents an exponential model to estimate CPF from the cough peak sound pressure level (CPSL). We investigated the relationship between cough sounds and cough flows and the effects of a measurement condition of cough sound, microphone type and participant’s height and gender on CPF estimation accuracy. The results confirmed that the proposed model estimated CPF with a high accuracy. The absolute error between CPFs and estimated CPFs were significantly lower when the microphone distance from the participant’s mouth was within 30 cm than when the distance exceeded 30 cm. Analysis of the model parameters showed that the estimation accuracy was not affected by participant’s height or gender. These results indicate that the proposed model has the potential to improve the feasibility of measuring and assessing CPF. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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