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Biosensors
Special Issues
Wearable Sensors for Precise Exercise Monitoring and Analysis
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Wearable Sensors for Precise Exercise Monitoring and Analysis

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2653

Special Issue Editors

Prof. Dr. Fengyu Li

E-Mail Website
Guest Editor
Chemistry and Materials Science, Jinan University, Guangzhou 510632, China
Interests: photonic crystal materials; multi-analyte sensing; flexible electronics; printed assembly; 3D printing manufacture
Dr. Meng Su

E-Mail Website
Guest Editor
Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing 100190, China
Interests: nano printing; nanophotonics; quantum dots; bio-detection; optoelectronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Developed countries, such as the U.S., have used a variety of apparatuses to monitor the vital signs of athletes and to analyze their physical kineses. Then, scientific coaching schemes and body recovery procedures are programmed based on this database of quantitative vital signs. Focused on the critical challenges of exercise monitoring and analysis, we aim to extend out knowledge of the dynamic conditions and multi-analyte sensing and analysis involved in this practice. The latest research advancement in “exercise monitoring” or “sports analysis” via “wearable sensors” will be collected in this Special Issue to present a general view of the precise physical assessments conducted during exercise training for athletes. The cross-subject nature and basic theories of metrology, nanomaterials, and kinesiology will allow us to develop a new analytical methodology. We also aim to promote China’s “national sport prosperousness”.

Prof. Dr. Fengyu Li
Dr. Meng Su
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. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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

  • exercise monitoring
  • sports analysis
  • wearable sensors
  • physical assessment
  • fatigue detection

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Published Papers (2 papers)

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Research

14 pages, 1580 KiB  
Article
Differential Measurement of Involuntary Breathing Movements
by Jacob Seman, Carlos Rodriguez Amaro, Lillian Tucker, Jordan M. Fleury, Keegan Erickson, Gannon White, Talles Batista Rattis Santos and Michelle M. Mellenthin
Biosensors 2025, 15(2), 87; https://doi.org/10.3390/bios15020087 - 5 Feb 2025
Viewed by 1093
Abstract
Free divers are known to experience a physiological response during extreme breath holding, causing involuntary breathing movements (IBMs). To investigate these movements, a low-cost multi-core ESP32-Pico microcontroller prototype was developed to measure IBMs during a static breath hold. This novel device, called the [...] Read more.
Free divers are known to experience a physiological response during extreme breath holding, causing involuntary breathing movements (IBMs). To investigate these movements, a low-cost multi-core ESP32-Pico microcontroller prototype was developed to measure IBMs during a static breath hold. This novel device, called the bioSense, uses a differential measurement between two accelerometers placed on the sternum and the xiphoid process to acquire breathing-related movements. Sensor placement allowed for data acquisition that was posture- and body-shape-agnostic. Sensor placement was also designed to be as non-intrusive as possible and precisely capture breathing movements at configurable sampling rates. Measurements from the device were sent over WiFi to be accessed on a password-protected webserver and backed up to a micro-secure digital (microSD) card. This device was used in a pilot study, where it captured the various phases of breathing experienced by recreational free divers alongside a force plate measurement system for comparison. Full article
(This article belongs to the Special Issue Wearable Sensors for Precise Exercise Monitoring and Analysis)
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23 pages, 10266 KiB  
Article
Application of Wearable Insole Sensors in In-Place Running: Estimating Lower Limb Load Using Machine Learning
by Shipan Lang, Jun Yang, Yong Zhang, Pei Li, Xin Gou, Yuanzhu Chen, Chunbao Li and Heng Zhang
Biosensors 2025, 15(2), 83; https://doi.org/10.3390/bios15020083 - 1 Feb 2025
Viewed by 1039
Abstract
Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related [...] Read more.
Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment and wearable devices, but datasets combining both sources are limited due to experimental complexities and signal synchronization challenges. Moreover, wearable-based algorithms often fail to capture deep signal features, hindering early detection and prevention of tibial fatigue injuries. In this study, we simultaneously collected data from laboratory equipment and wearable insole sensors during in-place running by volunteers, creating a dataset named WearLab-Leg. Based on this dataset, we developed a machine learning model integrating Temporal Convolutional Network (TCN) and Transformer modules to estimate vertical ground reaction force (vGRF) and tibia bone force (TBF) using insole pressure signals. Our model’s architecture effectively combines the advantages of local deep feature extraction and global modeling, and further introduces the Weight-MSELoss function to improve peak prediction performance. As a result, the model achieved a normalized root mean square error (NRMSE) of 7.33% for vGRF prediction and 10.64% for TBF prediction. Our dataset and proposed model offer a convenient solution for biomechanical monitoring in athletes and patients, providing reliable data and technical support for early warnings of fatigue-induced injuries. Full article
(This article belongs to the Special Issue Wearable Sensors for Precise Exercise Monitoring and Analysis)
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