A Diagnostic and Performance System for Soccer: Technical Design and Development
This study presents a novel system for diagnosing and evaluating soccer performance using wearable inertial sensors integrated into players’ insoles. Designed to meet the needs of professional podiatrists and sports practitioners, the system focuses on three key soccer-related movements: passing, sh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sports |
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| Online Access: | https://www.mdpi.com/2075-4663/13/1/10 |
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| author | Alberto Gascón Álvaro Marco David Buldain Javier Alfaro-Santafé Jose Victor Alfaro-Santafé Antonio Gómez-Bernal Roberto Casas |
| author_facet | Alberto Gascón Álvaro Marco David Buldain Javier Alfaro-Santafé Jose Victor Alfaro-Santafé Antonio Gómez-Bernal Roberto Casas |
| author_sort | Alberto Gascón |
| collection | DOAJ |
| description | This study presents a novel system for diagnosing and evaluating soccer performance using wearable inertial sensors integrated into players’ insoles. Designed to meet the needs of professional podiatrists and sports practitioners, the system focuses on three key soccer-related movements: passing, shooting, and changes of direction (CoDs). The system leverages low-power IMU sensors, Bluetooth Low Energy (BLE) communication, and a cloud-based architecture to enable real-time data analysis and performance feedback. Data were collected from nine professional players from the SD Huesca women’s team during controlled tests, and bespoke algorithms were developed to process kinematic data for precise event detection. Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. Compared to existing systems, this approach significantly reduces costs and enhances practicality by minimizing the number of sensors required while ensuring real-time evaluation capabilities. However, the study is limited by a small sample size, which restricts generalizability. Future research will aim to expand the dataset, include diverse sports, and integrate additional sensors for broader applications. This system offers a valuable tool for injury prevention, player rehabilitation, and performance optimization in professional soccer, bridging technical advancements with practical applications in sports science. |
| format | Article |
| id | doaj-art-74a991a4d76748bba07aba8824d1e02a |
| institution | Kabale University |
| issn | 2075-4663 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sports |
| spelling | doaj-art-74a991a4d76748bba07aba8824d1e02a2025-01-24T13:49:51ZengMDPI AGSports2075-46632025-01-011311010.3390/sports13010010A Diagnostic and Performance System for Soccer: Technical Design and DevelopmentAlberto Gascón0Álvaro Marco1David Buldain2Javier Alfaro-Santafé3Jose Victor Alfaro-Santafé4Antonio Gómez-Bernal5Roberto Casas6Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, SpainAragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, SpainAragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, SpainPodoactiva Research Department, 22197 Cuarte, SpainPodoactiva Research Department, 22197 Cuarte, SpainPodoactiva Research Department, 22197 Cuarte, SpainAragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, SpainThis study presents a novel system for diagnosing and evaluating soccer performance using wearable inertial sensors integrated into players’ insoles. Designed to meet the needs of professional podiatrists and sports practitioners, the system focuses on three key soccer-related movements: passing, shooting, and changes of direction (CoDs). The system leverages low-power IMU sensors, Bluetooth Low Energy (BLE) communication, and a cloud-based architecture to enable real-time data analysis and performance feedback. Data were collected from nine professional players from the SD Huesca women’s team during controlled tests, and bespoke algorithms were developed to process kinematic data for precise event detection. Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. Compared to existing systems, this approach significantly reduces costs and enhances practicality by minimizing the number of sensors required while ensuring real-time evaluation capabilities. However, the study is limited by a small sample size, which restricts generalizability. Future research will aim to expand the dataset, include diverse sports, and integrate additional sensors for broader applications. This system offers a valuable tool for injury prevention, player rehabilitation, and performance optimization in professional soccer, bridging technical advancements with practical applications in sports science.https://www.mdpi.com/2075-4663/13/1/10wearable technologymachine learning algorithmsinertial sensorssoccer biomechanicsfoot kinematics |
| spellingShingle | Alberto Gascón Álvaro Marco David Buldain Javier Alfaro-Santafé Jose Victor Alfaro-Santafé Antonio Gómez-Bernal Roberto Casas A Diagnostic and Performance System for Soccer: Technical Design and Development Sports wearable technology machine learning algorithms inertial sensors soccer biomechanics foot kinematics |
| title | A Diagnostic and Performance System for Soccer: Technical Design and Development |
| title_full | A Diagnostic and Performance System for Soccer: Technical Design and Development |
| title_fullStr | A Diagnostic and Performance System for Soccer: Technical Design and Development |
| title_full_unstemmed | A Diagnostic and Performance System for Soccer: Technical Design and Development |
| title_short | A Diagnostic and Performance System for Soccer: Technical Design and Development |
| title_sort | diagnostic and performance system for soccer technical design and development |
| topic | wearable technology machine learning algorithms inertial sensors soccer biomechanics foot kinematics |
| url | https://www.mdpi.com/2075-4663/13/1/10 |
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