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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: closed (15 June 2019).

Special Issue Editors

Dr. Wee Ser
Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Interests: Signal Processing
Dr. Nan Liu
Website
Guest Editor
Duke-NUS Medical School, National University of Singapore, Singapore
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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Keywords

  • wearable sensors
  • wearable devices
  • wearable techniques
  • healthcare

Published Papers (18 papers)

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Open AccessArticle
INSPEX: Optimize Range Sensors for Environment Perception as a Portable System
Sensors 2019, 19(19), 4350; https://doi.org/10.3390/s19194350 - 08 Oct 2019
Abstract
Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, [...] Read more.
Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, radar, ultrasound and visual) to detect various types of obstacles under different lighting and weather conditions, with the drawbacks of a given technology being offset by others. These systems require powerful computational capability to fuse the mass of data, which limits their use to high-end vehicles and robots. INSPEX delivers a low-power, small-size and lightweight environment perception system that is compatible with portable and/or wearable applications. This requires miniaturizing and optimizing existing range sensors of different technologies to meet the user’s requirements in terms of obstacle detection capabilities. These sensors consist of a LiDAR, a time-of-flight sensor, an ultrasound and an ultra-wideband radar with measurement ranges respectively of 10 m, 4 m, 2 m and 10 m. Integration of a data fusion technique is also required to build a model of the user’s surroundings and provide feedback about the localization of harmful obstacles. As primary demonstrator, the INSPEX device will be fixed on a white cane. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Simultaneous Measurement of Ear Canal Movement, Electromyography of the Masseter Muscle and Occlusal Force for Earphone-Type Occlusal Force Estimation Device Development
Sensors 2019, 19(15), 3441; https://doi.org/10.3390/s19153441 - 06 Aug 2019
Abstract
We intend to develop earphone-type wearable devices to measure occlusal force by measuring ear canal movement using an ear sensor that we developed. The proposed device can measure occlusal force during eating. In this work, we simultaneously measured the ear canal movement (ear [...] Read more.
We intend to develop earphone-type wearable devices to measure occlusal force by measuring ear canal movement using an ear sensor that we developed. The proposed device can measure occlusal force during eating. In this work, we simultaneously measured the ear canal movement (ear sensor value), the surface electromyography (EMG) of the masseter muscle and the occlusal force six times from five subjects as a basic study toward occlusal force meter development. Using the results, we investigated the correlation coefficient between the ear sensor value and the occlusal force, and the partial correlation coefficient between ear sensor values. Additionally, we investigated the average of the partial correlation coefficient and the absolute value of the average for each subject. The absolute value results indicated strong correlation, with correlation coefficients exceeding 0.9514 for all subjects. The subjects showed a lowest partial correlation coefficient of 0.6161 and a highest value of 0.8286. This was also indicative of correlation. We then estimated the occlusal force via a single regression analysis for each subject. Evaluation of the proposed method via the cross-validation method indicated that the root-mean-square error when comparing actual values with estimates for the five subjects ranged from 0.0338 to 0.0969. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Wireless, Portable Fiber Bragg Grating Interrogation System Employing Optical Edge Filter
Sensors 2019, 19(14), 3222; https://doi.org/10.3390/s19143222 - 22 Jul 2019
Cited by 6
Abstract
A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify [...] Read more.
A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify the plethysmograph signal, obtained as a small vibration of optical power on the large offset. The standard deviation of the measured Bragg wavelength was about 0.1 pm. The developed edge filter module and amplifier circuit were encased with a single-board computer and communicated with a laptop computer via Wi-Fi. As a result, the plethysmograph was clearly obtained remotely, indicating the possibility of continuous vital sign measurement. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Autonomic Nervous System Response during Light Physical Activity in Adolescents with Anorexia Nervosa Measured by Wearable Devices
Sensors 2019, 19(12), 2820; https://doi.org/10.3390/s19122820 - 24 Jun 2019
Cited by 5
Abstract
Anorexia nervosa (AN) is associated with a wide range of disturbances of the autonomic nervous system. The aim of the present study was to monitor the heart rate (HR) and the heart rate variability (HRV) during light physical activity in a group of [...] Read more.
Anorexia nervosa (AN) is associated with a wide range of disturbances of the autonomic nervous system. The aim of the present study was to monitor the heart rate (HR) and the heart rate variability (HRV) during light physical activity in a group of adolescent girls with AN and in age-matched controls using a wearable, minimally obtrusive device. For the study, we enrolled a sample of 23 adolescents with AN and 17 controls. After performing a 12-lead electrocardiogram and echocardiography, we used a wearable device to record a one-lead electrocardiogram for 5 min at baseline for 5 min during light physical exercise (Task) and for 5 min during recovery. From the recording, we extracted HR and HRV indices. Among subjects with AN, the HR increased at task and decreased at recovery, whereas among controls it did not change between the test phases. HRV features showed a different trend between the two groups, with an increased low-to-high frequency ratio (LF/HF) in the AN group due to increased LF and decreased HF, differently from controls that, otherwise, slightly increased their standard deviation of NN intervals (SDNN) and the root mean square of successive differences (RMSSD). The response in the AN group during the task as compared to that of healthy adolescents suggests a possible sympathetic activation or parasympathetic withdrawal, differently from controls. This result could be related to the low energy availability associated to the excessive loss of fat and lean mass in subjects with AN, that could drive to autonomic imbalance even during light physical activity. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors
Sensors 2019, 19(11), 2644; https://doi.org/10.3390/s19112644 - 11 Jun 2019
Cited by 3
Abstract
Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive [...] Read more.
Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Development of a Bendable Outsole Biaxial Ground Reaction Force Measurement System
Sensors 2019, 19(11), 2641; https://doi.org/10.3390/s19112641 - 11 Jun 2019
Cited by 5
Abstract
Wearable ground reaction force (GRF) measurement systems make it possible to measure the GRF in any environment, unlike a commercial force plate. When performing kinetic analysis with the GRF, measurement of multiaxial GRF is important for evaluating forward and lateral motion during natural [...] Read more.
Wearable ground reaction force (GRF) measurement systems make it possible to measure the GRF in any environment, unlike a commercial force plate. When performing kinetic analysis with the GRF, measurement of multiaxial GRF is important for evaluating forward and lateral motion during natural gait. In this paper, we propose a bendable GRF measurement system that can measure biaxial (vertical and anterior-posterior) GRF without interrupting the natural gait. Eight custom small biaxial force sensors based on an optical sensing mechanism were installed in the proposed system. The interference between two axes on the custom sensor was minimized by the independent application of a cantilever structure for the two axes, and the hysteresis and repeatability of the custom sensor were investigated. After developing the system by the installation of force sensors, we found that the degree of flexibility of the developed system was comparable to that of regular shoes by investigating the forefoot bending stiffness. Finally, we compared vertical GRF (vGRF) and anterior-posterior GRF (apGRF) measured from the developed system and force plate at the same time when the six subjects walked, ran, and jumped on the force plate to evaluate the performance of the GRF measurement system. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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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 - 24 Apr 2019
Cited by 7
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 - 18 Jan 2019
Cited by 3
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 - 14 Nov 2018
Cited by 6
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 - 12 Nov 2018
Cited by 10
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 - 10 Nov 2018
Cited by 8
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 - 08 Nov 2018
Cited by 9
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 - 07 Nov 2018
Cited by 4
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 - 12 Oct 2018
Cited by 2
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 - 12 Sep 2018
Cited by 14
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 - 31 Aug 2018
Cited by 28
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 - 22 Jul 2018
Cited by 3
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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Review

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Open AccessReview
Polymer Optical Fiber Sensors in Healthcare Applications: A Comprehensive Review
Sensors 2019, 19(14), 3156; https://doi.org/10.3390/s19143156 - 18 Jul 2019
Cited by 14
Abstract
Advances in medicine and improvements in life quality has led to an increase in the life expectancy of the general population. An ageing world population have placed demands on the use of assistive technology and, in particular, towards novel healthcare devices and sensors. [...] Read more.
Advances in medicine and improvements in life quality has led to an increase in the life expectancy of the general population. An ageing world population have placed demands on the use of assistive technology and, in particular, towards novel healthcare devices and sensors. Besides the electromagnetic field immunity, polymer optical fiber (POF) sensors have additional advantages due to their material features such as high flexibility, lower Young’s modulus (enabling high sensitivity for mechanical parameters), higher elastic limits, and impact resistance. Such advantages are well-aligned with the instrumentation requirements of many healthcare devices and in movement analysis. Aiming at these advantages, this review paper presents the state-of-the-art developments of POF sensors for healthcare applications. A plethora of healthcare applications are discussed, which include movement analysis, physiological parameters monitoring, instrumented insoles, as well as instrumentation of healthcare robotic devices such as exoskeletons, smart walkers, actuators, prostheses, and orthosis. This review paper shows the feasibility of using POF sensors in healthcare applications and, due to the aforementioned advantages, it is possible to envisage a further widespread use of such sensors in this research field in the next few years. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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