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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 7565

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Special Issue Editor

Dr. Manuel Mateo-March

E-Mail Website
Guest Editor

Department of Sport Sciences, Sports Research Center, Universidad Miguel Hernández de Elche, 03202 Alicante, Spain
Interests: physical and sports education

Special Issue Information

Dear Colleagues,

The Special Issue of Sensors dedicated to 'Sensors in Sports' aims to explore the latest advancements, challenges, and applications of sensor technology in the realm of sports. Sensors have revolutionized the way athletes approach training, competition, and performance tracking, offering valuable insights into biomechanics, physiology, and technique. This unique edition seeks to compile original research and review articles focusing on recent progressions, technologies, solutions, applications, and emerging challenges in the field of sports where sensors play a crucial role.

Potential topics for submission include, but are not limited to, the following:

Wearable sensor technologies for monitoring athlete performance;
Biomechanical analysis using motion sensors and inertial measurement units (IMUs);
Sensor-based approaches for injury prevention and rehabilitation in sports;
Smart textiles and wearable electronics for athlete monitoring and feedback;
Sensor systems for real-time tracking and analysis of sports movements;
Integration of sensor data with augmented reality (AR) and virtual reality (VR) for sports training;
Wireless sensor networks and applications of the Internet of Things (IoT) in sports;
Sensor-driven techniques for sports biomechanics research and optimizing performance;
Biofeedback systems and sensor-based coaching tools for athletes;
Remote monitoring and telemedicine applications in sports medicine;
Ethical considerations and privacy issues surrounding sensor-based sports analytics;
Case studies and practical implementations of sensor technology across various sports disciplines.

Dr. Manuel Mateo-March
Guest Editor

Manuscript Submission Information

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Keywords

wearable sensor technologies
inertial measurement units
rehabilitation

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Published Papers (5 papers)

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Research

13 pages, 2552 KiB

Open AccessArticle

Accuracy of an Ultra-Wideband-Based Tracking System for Time–Motion Analysis in Tennis

by Wenpu Yang, Jinzheng Wang, Zichen Zhao and Yixiong Cui

Sensors 2025, 25(4), 1031; https://doi.org/10.3390/s25041031 - 9 Feb 2025

Viewed by 836

Abstract

Player-tracking systems provide vital time–motion and tactical data for analyzing athletic performance. Ultra-wideband (UWB) systems are promising for racquet sports due to their accuracy and cost-effectiveness compared to GNSS and optical systems. This study evaluated the accuracy of a UWB tracking system (GenGee Insait KS) for tennis-specific movements by comparing it with an optical motion capture system (VICON). Ten amateur players (International Tennis Numbers: 2–5) participated, performing seven exercises, including warm-up, agility drills, and tactical drills, with and without racquets. Raw data from both systems were processed to calculate the distances traversed. The average root mean square error between the two systems was 0.65 m (X-axis) and 0.76 m (Y-axis). Significant measurement discrepancies were observed (standardized mean difference: 0.86–1.95), except for jogging and walking exercises (p > 0.05). The overall percentage error was 16.29%. The intraclass correlation coefficient for distance measurements was 0.91, indicating good reliability. Tasks involving rapid acceleration and directional changes, such as the spider run, exhibited larger errors (mean bias: 4.13 m, effect size: 1.03). While the UWB system demonstrated acceptable accuracy for steady movements, it showed notable discrepancies during high-speed, tennis-specific activities. Overestimation due to arm movement and hip rotation suggests caution when applying arm-mounted UWB devices in training and competitive settings. Full article

(This article belongs to the Special Issue Sensors in Sports)

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13 pages, 1560 KiB

Open AccessArticle

Monitoring Sleep and Nightly Recovery with Wrist-Worn Wearables: Links to Training Load and Performance Adaptations

by Olli-Pekka Nuuttila, Daniela Schäfer Olstad, Kaisu Martinmäki, Arja Uusitalo and Heikki Kyröläinen

Sensors 2025, 25(2), 533; https://doi.org/10.3390/s25020533 - 17 Jan 2025

Viewed by 1305

Abstract

Previous studies on the effects of intensified training on sleep quality/quantity have been somewhat contradictory. Moreover, recreational athletes often track various sleep metrics, and those metrics’ actual connections to training adaptations are unknown. This study explored the effects of intensified training on sleep and nightly recovery along with their associations with training adaptations. A total of 24 participants (10 females) performed a 3-week baseline training period (BL), a 2-week overload period (OL), and a 1-week recovery period (REC), which were followed by test days (T1–T3). The endurance performance was assessed with a 3000 m running test. Throughout all of the periods, the nightly recovery information was monitored with a wrist-worn wearable, including sleep quantity and quality, heart rate (HR) and HR variability (HRV), and proprietary parameters combining several parameters and scaling the results individually. In addition, the perceived strain and muscle soreness were evaluated daily. The 3000 m running performance improved from T1 to T2 (−1.2 ± 1.7%, p = 0.006) and from T1 to T3 (−1.7 ± 1.2%, p = 0.002). The perceived strain and muscle soreness increased (p < 0.001) from the final week of the BL to the final week of the OL, but the subjective sleep quality and nightly recovery metrics remained unchanged. The OL average of the proprietary parameter, autonomic nervous system charge (“ANS charge”, combining the HR, HRV, and breathing rate), as well as the change in the sleep HR and HRV from the BL to the OL, were associated (p < 0.05) with a change in the 3000 m running time. In conclusion, the subjective recovery metrics were impaired by intensified training, while the sleep and nightly recovery metrics showed no consistent changes. However, there were substantial interindividual differences in nightly recovery, which were also associated with the training adaptations. Therefore, monitoring nightly recovery can help in recognizing individual responses to training and assist in optimizing training prescriptions. Full article

(This article belongs to the Special Issue Sensors in Sports)

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12 pages, 1591 KiB

Open AccessArticle

Do Power Meter Data Depend on the Device on Which They Are Collected? Comparison of Eleven Different Recordings

by José-Antonio Salas-Montoro, Ignacio Valdivia-Fernández, Alejandro de Rozas, José-Manuel Reyes-Sánchez, Mikel Zabala and Juan-José Pérez-Díaz

Sensors 2025, 25(2), 295; https://doi.org/10.3390/s25020295 - 7 Jan 2025

Cited by 2 | Viewed by 1623

Abstract

This study evaluated the influence of cycle computers on the accuracy of power and cadence data. The research was divided into three phases: (1) a graded exercise test (GXT) at different constant loads to record power and cadence data; (2) a self-paced effort lasting 1 min to measure mean maximal power output (MMP); and (3) a short all-out effort. Eight cyclists completed the GXT, ten participated in the 1-min test, and thirty participated in the sprint effort. All participants pedaled on a controlled-resistance cycle ergometer, and the data were recorded using the ergometer itself and ten synchronized cycle computers of the same brand, configured to record at 1 Hz. The results showed minimal variations in power and cadence between devices during the GXT, suggesting adequate accuracy for constant efforts lasting a certain duration. However, in self-paced and high-intensity efforts (1-min and short all-out efforts), significant differences were observed between several devices, particularly in cadence and mean power, highlighting the relevance of device selection in these contexts. These findings suggest that, while variations in constant efforts may be negligible, in short-duration, high-intensity activities, the choice of device may be crucial for the accuracy and reliability of the data. Full article

(This article belongs to the Special Issue Sensors in Sports)

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17 pages, 2545 KiB

Open AccessArticle

Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of Soccer

by Arian Skoki, Stefan Ivić, Sandi Ljubic, Jonatan Lerga and Ivan Štajduhar

Sensors 2024, 24(24), 8128; https://doi.org/10.3390/s24248128 - 19 Dec 2024

Viewed by 866

Abstract

In the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters. Our refined model achieved a slight improvement in the

R^{2}

score in the maximum hand-grip test, increasing from

0.87

0.89

compared to the existing model. It also demonstrated dynamic change robustness in a soccer-specific 1 min drill and 15 min treadmill protocol extracted from the literature. Through individualized fitting on a 10-repetition 80 m sprint test for a soccer player, the model exhibited

R^{2}

scores between

0.62

and

0.80

. Furthermore, when tested with actual soccer match data, it maintained a robust performance, with the average

R^{2}

scores ranging from

0.70

0.72

. The proposed approach holds the potential to advance the understanding of tactical decisions by correlating them with real-time physical performance, offering opportunities for more informed strategies and ultimately enhancing team performance. Full article

(This article belongs to the Special Issue Sensors in Sports)

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14 pages, 1993 KiB

Open AccessArticle

Weekly External Load Correlation in Season Microcycles with Game Running Performance and Training Quantification in Elite Young Soccer Players

by Vasileios Kanaras, Yiannis Michailidis, Athanasios Mandroukas, Andreas Stafylidis, Lazaros Vardakis, Angelos E. Kyranoudis, Kosmas Christoulas, Ioannis Gissis and Thomas I. Metaxas

Sensors 2024, 24(14), 4523; https://doi.org/10.3390/s24144523 - 12 Jul 2024

Cited by 3 | Viewed by 1984

Abstract

The purpose of this study was to (a) correlate the weekly external training load with the game running performance in season microcycles and (b) specify the optimal training/game ratio of the weekly external load in elite youth soccer players. The total distance (TD), the high-speed running distance (HSRD) (19.8–25.2 km/h), the ZONE6 distance (>25.2 km/h), the acceleration (ACC) (≥+2 m/s²), and the deceleration (DEC) (≥−2 m/s²) were monitored with global positioning system (GPS) technology throughout 18 microcycles and official games. TD had a very high positive correlation average (r = 0.820, p = 0.001), the HSRD had a high positive correlation average (r = 0.658, p = 0.001), the ZONE6 distance and DEC had a moderate positive correlation average ((r = 0.473, p = 0.001) and (r = 0.478, p = 0.001), respectively), and the ACC had a low positive correlation average (r = 0.364, p = 0.001) between microcycles and games. Regarding the training/game ratio, the HSRD showed statistically significant differences between ratios 1.43 and 2.60 (p = 0.012, p ≤ 0.05), the ACC between ratios 2.42 and 4.45 (p = 0.050, p ≤ 0.05) and ratios 3.29 and 4.45 (p = 0.046, p ≤ 0.05), and the DEC between ratios 2.28 and 3.94 (p = 0.034, p ≤ 0.05). Considering the correlation between weekly training and game external load, high weekly training TD values correspond to higher game values, whereas HSRD, ZONE6 distance, ACC, and DEC, which determine training intensity, should be trained in a specific volume. Training/game ratios of 1.43, 2.42 to 3.29, and 2.28 to 3.11 seem to be optimal for HSRD, ACC, and DEC weekly training, respectively. Full article

(This article belongs to the Special Issue Sensors in Sports)

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