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Wearable Biomedical Devices and Sensors
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Wearable Biomedical Devices and Sensors

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 9787

Special Issue Editors

Prof. Dr. Carlo Menon

E-Mail Website
Guest Editor
Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
Interests: biomedical technology; wearables; health technology; medical signal analysis; sensorimotor recovery; neurorehabilitation; electronic textiles
Special Issues, Collections and Topics in MDPI journals
Dr. Xianta Jiang

E-Mail Website
Guest Editor
Department of Computer Science Memorial University of Newfoundland, St John's, NL, Canada
Interests: intelligent human machine interaction human behavior recognition bio-signal processing eye-tracking and pupil diameter human factors; prosthetic interface; wearables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable biomedical sensors and devices hold promise to tackle major challenges in healthcare. Potential applications of wearable technologies in biomedicince include early diagnosis of diseases such as congestive heart failure, the prevention of chronic conditions such as diabetes, improved clinical management of neurodegenerative conditions such as Parkinson's disease, and the ability to promptly respond to emergency situations such as seizures in patients with epilepsy and cardiac arrest in subjects undergoing cardiovascular monitoring.

This Special issue focuses on all aspects of research and development related to wearable biomedical sensors and devices. Original, high-quality contributions that have not yet been published and that are not currently under review by other journals or peer-reviewed conferences are sought.

Prof. Dr. Carlo Menon
Dr. Xianta Jiang
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • Biomedical instrumentation
  • Noninvasive wearables
  • Wearable biosensors
  • Artificial Intelligence
  • The Internet of Medical Things
  • Electrophysiology
  • Vital signs
  • Body sensor network

Published Papers (5 papers)

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Research

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11 pages, 3987 KiB  
Article
Wearable Noninvasive Glucose Sensor Based on CuxO NFs/Cu NPs Nanocomposites
by Zhipeng Yu, Huan Wu, Zhongshuang Xu, Zhimao Yang, Jian Lv and Chuncai Kong
Sensors 2023, 23(2), 695; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020695 - 07 Jan 2023
Cited by 4 | Viewed by 1644
Abstract
Designing highly active material to fabricate a high-performance noninvasive wearable glucose sensor was of great importance for diabetes monitoring. In this work, we developed CuxO nanoflakes (NFs)/Cu nanoparticles (NPs) nanocomposites to serve as the sensing materials for noninvasive sweat-based wearable glucose [...] Read more.
Designing highly active material to fabricate a high-performance noninvasive wearable glucose sensor was of great importance for diabetes monitoring. In this work, we developed CuxO nanoflakes (NFs)/Cu nanoparticles (NPs) nanocomposites to serve as the sensing materials for noninvasive sweat-based wearable glucose sensors. We involve CuCl2 to enhance the oxidation of Cu NPs to generate Cu2O/CuO NFs on the surface. Due to more active sites endowed by the CuxO NFs, the as-prepared sample exhibited high sensitivity (779 μA mM−1 cm−2) for noninvasive wearable sweat sensing. Combined with a low detection limit (79.1 nM), high selectivity and the durability of bending and twisting, the CuxO NFs/Cu NPs-based sensor can detect the glucose level change of sweat in daily life. Such a high-performance wearable sensor fabricated by a convenient method provides a facile way to design copper oxide nanomaterials for noninvasive wearable glucose sensors. Full article
(This article belongs to the Special Issue Wearable Biomedical Devices and Sensors)
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10 pages, 2839 KiB  
Article
PET/ZnO@MXene-Based Flexible Fabrics with Dual Piezoelectric Functions of Compression and Tension
by Yanlu Chen, Xinxin Pu, Xinyu Xu, Menghan Shi, Hui-Jun Li and Ding Wang
Sensors 2023, 23(1), 91; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010091 - 22 Dec 2022
Cited by 5 | Viewed by 1970
Abstract
The traditional self-supported piezoelectric thin films prepared by filtration methods are limited in practical applications due to their poor tensile properties. The strategy of using flexible polyethylene terephthalate (PET) fabric as the flexible substrate is beneficial to enhancing the flexibility and stretchability of [...] Read more.
The traditional self-supported piezoelectric thin films prepared by filtration methods are limited in practical applications due to their poor tensile properties. The strategy of using flexible polyethylene terephthalate (PET) fabric as the flexible substrate is beneficial to enhancing the flexibility and stretchability of the flexible device, thus extending the applications of pressure sensors. In this work, a novel wearable pressure sensor is prepared, of which uniform and dense ZnO nanoarray-coated PET fabrics are covered by a two-dimensional MXene nanosheet. The ternary structure incorporates the advantages of the three components including the superior piezoelectric properties of ZnO nanorod arrays, the excellent flexibility of the PET substrate, and the outstanding conductivity of MXene, resulting in a novel wearable sensor with excellent pressure-sensitive properties. The PET/ZnO@MXene pressure sensor exhibits excellent sensing performance (S = 53.22 kPa−1), fast response/recovery speeds (150 ms and 100 ms), and superior flexural stability (over 30 cycles at 5% strain). The composite fabric also shows high sensitivity in both motion monitoring and physiological signal detection (e.g., device bending, elbow bending, finger bending, wrist pulse peaks, and sound signal discrimination). These findings provide insight into composite fabric-based pressure-sensitive materials, demonstrating the great significance and promising prospects in the field of flexible pressure sensing. Full article
(This article belongs to the Special Issue Wearable Biomedical Devices and Sensors)
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13 pages, 3714 KiB  
Communication
Improved Wearable Devices for Dietary Assessment Using a New Camera System
by Mingui Sun, Wenyan Jia, Guangzong Chen, Mingke Hou, Jiacheng Chen and Zhi-Hong Mao
Sensors 2022, 22(20), 8006; https://0-doi-org.brum.beds.ac.uk/10.3390/s22208006 - 20 Oct 2022
Cited by 5 | Viewed by 2085
Abstract
An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and [...] Read more.
An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased. In recent years, as electronics, information, and artificial intelligence (AI) technologies advanced rapidly, image-based objective dietary assessment using wearable electronic devices has become a powerful approach. However, research in this field has been focused on the developments of advanced algorithms to process image data. Few reports exist on the study of device hardware for the particular purpose of dietary assessment. In this work, we demonstrate that, with the current hardware design, there is a considerable risk of missing important dietary data owing to the common use of rectangular image screen and fixed camera orientation. We then present two designs of a new camera system to reduce data loss by generating circular images using rectangular image sensor chips. We also present a mechanical design that allows the camera orientation to be adjusted, adapting to differences among device wearers, such as gender, body height, and so on. Finally, we discuss the pros and cons of rectangular versus circular images with respect to information preservation and data processing using AI algorithms. Full article
(This article belongs to the Special Issue Wearable Biomedical Devices and Sensors)
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14 pages, 391 KiB  
Article
Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease
by Ghazal Farhani, Yue Zhou, Mary E. Jenkins, Michael D. Naish and Ana Luisa Trejos
Sensors 2022, 22(19), 7322; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197322 - 27 Sep 2022
Cited by 5 | Viewed by 1966
Abstract
Hand tremor is one of the dominating symptoms of Parkinson’s disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are [...] Read more.
Hand tremor is one of the dominating symptoms of Parkinson’s disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types. Full article
(This article belongs to the Special Issue Wearable Biomedical Devices and Sensors)
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Other

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14 pages, 6525 KiB  
Protocol
Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation
by Rafael Castro Aguiar, Edward Jero Sam Jeeva Raj and Samit Chakrabarty
Sensors 2023, 23(9), 4340; https://0-doi-org.brum.beds.ac.uk/10.3390/s23094340 - 27 Apr 2023
Cited by 1 | Viewed by 1183
Abstract
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained [...] Read more.
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies. Full article
(This article belongs to the Special Issue Wearable Biomedical Devices and Sensors)
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