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

Deadline for manuscript submissions: closed (15 September 2021) | Viewed by 53327

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Special Issue Editors

Prof. Dr. Fernando Seoane

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Guest Editor

1. Department for Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
2. Department of Medical Care Technologies, Karolinska University Hospital, Stockholm, Sweden
3. Swedish School of Textiles, University of Borås, Borås, Sweden
Interests: Electrical Bioimpedance Smart Textiles; Biomedical Engineering; Biomedical Instrumentation; Biomedical Signal Processing; Wearable Sensors
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wei Chen

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Guest Editor

School of Information Science and Technology, Fudan University, Shanghai 200433, China
Interests: medical monitoring system; patient health monitoring; neonatal monitoring; brain activity monitoring; smart sleep; smart rehabilitation system; wireless body area networks Photo:
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in flexible materials, such as textile materials and manufacturing methods together with developments in sensing technology benefiting from the blooming of IoT have fostered new healthcare applications and solutions, both at point-of-care and in pervasive healthcare.

In this Special Issue, we call for papers presenting advances in wearable sensing and data processing with a highly plausible potential to enable novel p health solutions from novel transducer materials, to new data analytics and visualization methods through textile-electronic integration techniques or technologies for biomedical sensing

Prof. Dr. Fernando Seoane
Prof. Dr. Chen Wei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

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Keywords

wearable sensors
smart textiles
biomedical instrumentation
textile-electronic integration
data processing of wearable sensor systems
m-health
pervasive health

Published Papers (11 papers)

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Research

17 pages, 7740 KiB

Open AccessArticle

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography

by Chang-Lin Hu, I-Cheng Cheng, Chih-Hsien Huang, Yu-Te Liao, Wei-Chieh Lin, Kun-Ju Tsai, Chih-Hsien Chi, Chang-Wen Chen, Chia-Hsi Wu, I-Te Lin, Chien-Ju Li and Chii-Wann Lin

Sensors 2021, 21(20), 6789; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206789 - 13 Oct 2021

Cited by 7 | Viewed by 3174

Abstract

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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20 pages, 4036 KiB

Open AccessArticle

On the Use of Movement-Based Interaction with Smart Textiles for Emotion Regulation

by Mengqi Jiang, Vijayakumar Nanjappan, Martijn ten Bhömer and Hai-Ning Liang

Sensors 2021, 21(3), 990; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030990 - 02 Feb 2021

Cited by 6 | Viewed by 4585

Abstract

Research from psychology has suggested that body movement may directly activate emotional experiences. Movement-based emotion regulation is the most readily available but often underutilized strategy for emotion regulation. This research aims to investigate the emotional effects of movement-based interaction and its sensory feedback mechanisms. To this end, we developed a smart clothing prototype, E-motionWear, which reacts to four movements (elbow flexion/extension, shoulder flexion/extension, open and closed arms, neck flexion/extension), fabric-based detection sensors, and three-movement feedback mechanisms (audio, visual and vibrotactile). An experiment was conducted using a combined qualitative and quantitative approach to collect participants’ objective and subjective emotional feelings. Results indicate that there was no interaction effect between movement and feedback mechanism on the final emotional results. Participants preferred vibrotactile and audio feedback rather than visual feedback when performing these four kinds of upper body movements. Shoulder flexion/extension and open-closed arm movements were more effective for improving positive emotion than elbow flexion/extension movements. Participants thought that the E-motionWear prototype were comfortable to wear and brought them new emotional experiences. From these results, a set of guidelines were derived that can help frame the design and use of smart clothing to support users’ emotional regulation. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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18 pages, 6683 KiB

Open AccessArticle

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

by Long Meng, Anjing Zhang, Chen Chen, Xingwei Wang, Xinyu Jiang, Linkai Tao, Jiahao Fan, Xuejiao Wu, Chenyun Dai, Yiyuan Zhang, Bart Vanrumste, Toshiyo Tamura and Wei Chen

Sensors 2021, 21(3), 799; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030799 - 26 Jan 2021

Cited by 18 | Viewed by 3304

Abstract

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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13 pages, 7243 KiB

Open AccessArticle

Strength Training Characteristics of Different Loads Based on Acceleration Sensor and Finite Element Simulation

by Bo Pang, Zhongqiu Ji, Zihua Zhang, Yunchuan Sun, Chunmin Ma, Zirong He, Xin Hu and Guiping Jiang

Sensors 2021, 21(2), 647; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020647 - 19 Jan 2021

Cited by 8 | Viewed by 2421

Abstract

Deep squat, bench press and hard pull are important ways for people to improve their strength. The use of sensors to measure force is rare. Measuring strength with sensors is extremely valuable for people to master the intensity of exercise to scientifically effective exercise. To this end, in this paper, we used a real-time wireless motion capture and mechanical evaluation system of the wearable sensor to measure the dynamic characteristics of 30 young men performing deep squat, bench press and hard pull maneuvers. The data of tibia were simulated with AnyBody 5.2 and ANSYS 19.2 to verify the authenticity. The result demonstrated that the appropriate force of the deep squat elbow joint, the hip joint and the knee joint is 40% 1RM, the appropriate force of the bench press is 40% 1RM and the appropriate force of the hard pull is 80% 1RM. The external force is the main factor of bone change. The mechanical characteristics of knee joint can be simulated after the Finite Element Analysis and the simulation of AnyBody model are verified. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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23 pages, 5123 KiB

Open AccessArticle

Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration

by Xinyue Lu, Christine Azevedo Coste, Marie-Cécile Nierat, Serge Renaux, Thomas Similowski and David Guiraud

Sensors 2021, 21(1), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010099 - 25 Dec 2020

Cited by 6 | Viewed by 2474

Abstract

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed—Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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15 pages, 7942 KiB

Open AccessArticle

Development of Washable Silver Printed Textile Electrodes for Long-Term ECG Monitoring

by Abreha Bayrau Nigusse, Benny Malengier, Desalegn Alemu Mengistie, Granch Berhe Tseghai and Lieva Van Langenhove

Sensors 2020, 20(21), 6233; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216233 - 31 Oct 2020

Cited by 35 | Viewed by 4385

Abstract

Long-term electrocardiography (ECG) monitoring is very essential for the early detection and treatment of cardiovascular disorders. However, commercially used silver/silver chloride (Ag/AgCl) electrodes have drawbacks, and these become more obvious during long-term signal monitoring, making them inconvenient for this use. In this study, we developed silver printed textile electrodes from knitted cotton and polyester fabric for ECG monitoring. The surface resistance of printed electrodes was 1.64 Ω/sq for cotton and 1.78 Ω/sq for polyester electrodes. The ECG detection performance of the electrodes was studied by placing three electrodes around the wrist where the electrodes were embedded on an elastic strap with Velcro. The ECG signals collected using textile electrodes had a comparable waveform to those acquired using standard Ag/AgCl electrodes with a signal to noise ratio (SNR) of 33.10, 30.17, and 33.52 dB for signals collected from cotton, polyester, and Ag/AgCl electrodes, respectively. The signal quality increased as the tightness of the elastic strap increased. Signals acquired at 15 mmHg pressure level with the textile electrodes provided a similar quality to those acquired using standard electrodes. Interestingly, the textile electrodes gave acceptable signal quality even after ten washing cycles. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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17 pages, 5800 KiB

Open AccessArticle

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

by Shumi Zhao, Jianxun Liu, Zidan Gong, Yisong Lei, Xia OuYang, Chi Chiu Chan and Shuangchen Ruan

Sensors 2020, 20(17), 4861; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174861 - 28 Aug 2020

Cited by 29 | Viewed by 5463

Abstract

Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) via a low-energy Bluetooth module. A software platform was developed for data analysis to visualize ECG/EMG information, and integrated into the robotic glove control module. In the training progress, various hand activities (i.e., hand closing, forearm pronation, finger flexion, and wrist extension) were monitored by the EMG sensor, and the changes in the physiological status of people (from excited to fatigue) were monitored by the ECG sensor. The functionality and feasibility of the developed physiological monitoring system was demonstrated by the assisting robotic glove with an adaptive strategy for upper limb rehabilitation training improvement. The feasible results provided a novel technique to monitor individual ECG and EMG information holistically and practically, and a technical reference to improve upper limb rehabilitation according to specific treatment conditions and the users’ demands. On the basis of this wearable monitoring system prototype for upper limb rehabilitation, many ECG-/EMG-based mobile healthcare applications could be built avoiding some complicated implementation issues such as sensors management and feature extraction. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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22 pages, 8056 KiB

Open AccessArticle

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

by Bo Zhou, Alejandro Baucells Costa and Paul Lukowicz

Sensors 2020, 20(15), 4234; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154234 - 29 Jul 2020

Cited by 5 | Viewed by 6026

Abstract

Cardiorespiratory (CR) signals are crucial vital signs for fitness condition tracking, medical diagnosis, and athlete performance evaluation. Monitoring such signals in real-life settings is among the most widespread applications of wearable computing. We investigate how miniaturized barometers can be used to perform accurate spirometry in a wearable system that is built on off-the-shelf training masks often used by athletes as a training aid. We perform an evaluation where differential barometric pressure sensors are compared concurrently with a digital spirometer, during an experimental setting of clinical forced vital capacity (FVC) test procedures with 20 participants. The relationship between the two instruments is derived by mathematical modeling first, then by various regression methods from experiment data. The results show that the error of FVC vital values between the two instruments can be as low as 2∼3%. Beyond clinical tests, the method can also measure continuous tidal breathing air volumes with a 1∼3% error margin. Overall, we conclude that barometers with millimeter footprints embedded in face mask apparel can perform similarly to a digital spirometer to monitor breathing airflow and volume in pulmonary function tests. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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16 pages, 2869 KiB

Open AccessArticle

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

by Nicholas C. Jacobson and Yeon Joo Chung

Sensors 2020, 20(12), 3572; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123572 - 24 Jun 2020

Cited by 62 | Viewed by 5764

Abstract

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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16 pages, 3053 KiB

Open AccessArticle

Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

by Sherif Said, Ilyes Boulkaibet, Murtaza Sheikh, Abdullah S. Karar, Samer Alkork and Amine Nait-ali

Sensors 2020, 20(11), 3144; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113144 - 02 Jun 2020

Cited by 29 | Viewed by 8317

Abstract

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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13 pages, 2966 KiB

Open AccessArticle

Design and Evaluation of Magnetic Hall Effect Tactile Sensors for Use in Sensorized Splints

by Dominic Jones, Lefan Wang, Ali Ghanbari, Vasiliki Vardakastani, Angela E. Kedgley, Matthew D. Gardiner, Tonia L. Vincent, Peter R. Culmer and Ali Alazmani

Sensors 2020, 20(4), 1123; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041123 - 19 Feb 2020

Cited by 17 | Viewed by 6209

Abstract

Splinting techniques are widely used in medicine to inhibit the movement of arthritic joints. Studies into the effectiveness of splinting as a method of pain reduction have generally yielded positive results, however, no significant difference has been found in clinical outcomes between splinting types. Tactile sensing has shown great promise for the integration into splinting devices and may offer further information into applied forces to find the most effective methods of splinting. Hall effect-based tactile sensors are of particular interest in this application owing to their low-cost, small size, and high robustness. One complexity of the sensors is the relationship between the elastomer geometry and the measurement range. This paper investigates the design parameters of Hall effect tactile sensors for use in hand splinting. Finite element simulations are used to locate the areas in which sensitivity is high in order to optimise the deflection range of the sensor. Further simulations then investigate the mechanical response and force ranges of the elastomer layer under loading which are validated with experimental data. A 4 mm radius, 3 mm-thick sensor is identified as meeting defined sensing requirements for range and sensitivity. A prototype sensor is produced which exhibits a pressure range of 45 kPa normal and 6 kPa shear. A proof of principle prototype demonstrates how this can be integrated to form an instrumented splint with multi-axis sensing capability and has the potential to inform clinical practice for improved splinting. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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