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Sports Sensors for Athlete Motion Tracking and Physiological Monitoring: Second Edition

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

Deadline for manuscript submissions: 25 November 2025 | Viewed by 755

Special Issue Editors

Dr. Carlos D. Gómez-Carmona

E-Mail Website
Guest Editor
1. Optimization of Training and Sports Performance (GOERD), Department of Didactics of Music Plastic and Body Expression, Faculty of Sport Science, University of Extremadura, 10071 Caceres, Spain
2. Biovetmed & Sportsci Research Group, Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, 30720 San Javier, Spain
Interests: sports performance; technology; external and internal workload; training and competition context; individual and team sports; data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Pino Ortega

E-Mail Website
Guest Editor
Biovetmed & Sportsci Research Group, Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, 30720 San Javier, Spain
Interests: sports training; excise science; physical activity; tracking technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sergio José Ibáñez Godoy

E-Mail Website
Guest Editor
Grupo de Optimización del Entrenamiento y Rendimiento Deportivo (GOERD), Facultad de Ciencias del Deporte, Universidad de Extremadura, 06006 Badajoz, Spain
Interests: sports pedagogy; physical education; coach education; coaching science; sports; sports training; sports performance; performance analysis; notational analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the second edition of this Special Issue. You can view the original Special Issue at https://www.mdpi.com/journal/sensors/special_issues/NT99M9CZL6. Monitoring athlete performance and health has become increasingly important in competitive sports. Advances in sensor technologies over recent decades have enabled the more precise and comprehensive tracking of both internal and external metrics during training and competition. This Special Issue provides a timely overview of state-of-the-art sensing capabilities for sporting applications and an analysis of the data such systems generate. The papers within this collection examine the latest developments across a range of sensor types, including wearable devices and integrated smart equipment, used to measure critical internal load parameters such as heart rate, muscle oxygenation, blood lactate, etc. This Special Issue also covers recent innovations in external workload monitoring, from GPS tracking systems, radio frequency-based position trackers, accelerometers, gyroscopes, and magnetometers embedded in equipment and clothing to smartphone apps utilizing the device’s cameras and sensors. Additionally, this Special Issue also focuses on optical systems like photocells, laser distance trackers, and video analysis to quantify biomechanics, speed, and distances covered at high resolutions. With sensor miniaturization and greater affordability improving accessibility, the papers in this Special Issue assess effective implementation, data management, and analysis techniques as well as the emerging trends pointing toward an ever more quantified future for athletes and sports medicine practitioners alike. Altogether, this collection offers valuable insights for research and practice relating to this modern frontier of sports performance optimization.

Dr. Carlos D. Gómez-Carmona
Prof. Dr. José Pino Ortega
Prof. Dr. Sergio José Ibáñez Godoy
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

  • sport training
  • GPS tracking systems
  • radio frequency-based position trackers
  • athlete monitoring
  • athlete motion tracking
  • laser distance trackers
  • inertial measurement unit
  • video analysis
  • wearable devices
  • physical and physiological analysis

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Published Papers (3 papers)

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Research

15 pages, 5553 KiB  
Article
Design and Development of a Public AI Referee Assistance System Based on Harmony OS Platform
by Jingjing Zhao, Chang Zhu, Bo Leng and Jiantao Qi
Sensors 2025, 25(7), 2127; https://doi.org/10.3390/s25072127 - 27 Mar 2025
Viewed by 75
Abstract
The Hawkeye system was regarded as a successful and effective referee assistant in commercial applications. However, the high hardware investment and strong professionalism set up a physical hurdle in public application. In this sense, one AI referee assistance system is a small-scale system [...] Read more.
The Hawkeye system was regarded as a successful and effective referee assistant in commercial applications. However, the high hardware investment and strong professionalism set up a physical hurdle in public application. In this sense, one AI referee assistance system is a small-scale system developed under the guidance of computer vision theory and technology, using deep learning-based object detection algorithms and computer graphics-related knowledge. Its main function is to use computer vision technology to make correct and fair judgments on controversial decisions in badminton matches. The AI referee assistance system uses the Harmony OS platform to build a frontend mini-program and uses the YOLOV5 algorithm to detect and track corresponding targets, ultimately achieving judgments both inside and outside the bounds as well as serving violations. In this study, it was found that the object detection model trained in this study performed well, with a prediction accuracy of up to 99%. For posture recognition, this study also utilizes the relatively mature MediaPipe algorithm, which ensures high accuracy of the entire intelligent referee system’s ruling and meets the needs of current students. This helps college students better participate in badminton training and competitions, further improving their enthusiasm for participating in sports and, to some extent, solving the problem of scarce referee resources. Full article
(This article belongs to the Special Issue Sports Sensors for Athlete Motion Tracking and Physiological Monitoring: Second Edition)
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13 pages, 1081 KiB  
Article
Quantifying the Effects of Detraining on Female Basketball Players Using Physical Fitness Assessment Sensors
by Enrique Flórez-Gil, Alejandro Vaquera, Daniele Conte and Alejandro Rodríguez-Fernández
Sensors 2025, 25(7), 1967; https://doi.org/10.3390/s25071967 - 21 Mar 2025
Viewed by 139
Abstract
This study leverages physical fitness assessment sensors to investigate the effects of a brief in-season break (detraining period) on the physical performance of female basketball players. Sixty-seven players (Senior (n = 19), U18 (n = 19), and U14 (n = 29)) were evaluated [...] Read more.
This study leverages physical fitness assessment sensors to investigate the effects of a brief in-season break (detraining period) on the physical performance of female basketball players. Sixty-seven players (Senior (n = 19), U18 (n = 19), and U14 (n = 29)) were evaluated before and after a 3-week break using sensor-derived data from a countermovement jump (CMJ), an Abalakov jump (ABK), a linear speed test (20 m sprint), a seated medicine ball throw test (SMBT), and a Basketball-Specific Agility Test (TEA-Basket). The Total Score of Athleticism (TSA), computed as the mean Z-Score across tests, served as a composite indicator of physical fitness. Data obtained from performance sensors revealed significant interactions between time and category for the CMJ, ABK, 20 m sprint, and SMBT, while TEA-Basket measurements showed no significant changes. Time and baseline fitness level interactions were also significant for the CMJ, ABK, and SMBT but not for sprint time or the TEA-Basket. Despite observed declines in explosive strength, speed, and upper-body power across all groups, TSA scores remained stable. These findings underscore the utility of sensor-based evaluation methods in highlighting the adverse effects of short-term detraining and emphasize the necessity of tailored training strategies during competitive breaks. Full article
(This article belongs to the Special Issue Sports Sensors for Athlete Motion Tracking and Physiological Monitoring: Second Edition)
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20 pages, 1240 KiB  
Article
Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition
by Robbe Decorte, Jelle Vanhaeverbeke, Sarah VanDen Berghe, Maarten Slembrouck and Steven Verstockt
Sensors 2025, 25(6), 1828; https://doi.org/10.3390/s25061828 - 14 Mar 2025
Viewed by 321
Abstract
This paper explores the use of wearable technology (Garmin Fenix 7) to monitor physiological and psychological factors contributing to attrition during basic military training. Attrition, or the voluntary departure of recruits from the military, often results from physical and psychological challenges, such as [...] Read more.
This paper explores the use of wearable technology (Garmin Fenix 7) to monitor physiological and psychological factors contributing to attrition during basic military training. Attrition, or the voluntary departure of recruits from the military, often results from physical and psychological challenges, such as fatigue, injury, and stress, which lead to significant costs for the military. To better understand and mitigate attrition, we designed and implemented a comprehensive and continuous data-capturing methodology to monitor 63 recruits during their basic infantry training. It’s optimized for military use by being minimally invasive (for both recruits and operators), preventing data leakage, and being built for scale. We analysed data collected from two test phases, focusing on seven key psychometric and physical features derived from baseline questionnaires and physiological measurements from wearable devices. The preliminary results revealed that recruits at risk of attrition tend to cluster in specific areas of the feature space in both Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Key indicators of attrition included low motivation, low resilience, and a stress mindset. Furthermore, we developed a predictive model using physiological data, such as sleep scores and step counts from Garmin devices, achieving a macro mean absolute error (MAE) of 0.74. This model suggests the potential to reduce the burden of daily wellness questionnaires by relying on continuous, unobtrusive monitoring. Full article
(This article belongs to the Special Issue Sports Sensors for Athlete Motion Tracking and Physiological Monitoring: Second Edition)
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