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<p>A comprehensive assessment of the physical capabilities and attributes of soccer players is essential for effective college soccer training. The ability to implement targeted tactical training relies on a thorough understanding of each player’s physical condition. This paper presents a monitoring system for soccer players’ bodily functions, developed using inertial sensor technology alongside Beer’s Law, tailored to recognize human body movement states and to measure blood oxygen and heart rate. The system’s efficacy in movement recognition and physiological monitoring was evaluated through comparative testing, assessing its performance against established algorithms. Notably, this system demonstrated a 9.75% improvement in the average accuracy for recognizing walking and running actions compared to the Juha Parkka decision tree algorithm and a 6.5% increase in accuracy over the D.M. Karantonis algorithm for walking action recognition. Heart rate measurements incurred an error of approximately ±2 BPM, with a maximum error of 1.7, and oxygen saturation monitoring displayed an error margin of ±2%, with a maximum deviation of 1.6. These deviations fall within acceptable limits. The results of this study furnish valuable data to support the customization of training content and intensity for college soccer athletes and offer insights for optimizing player selection and tactical strategies during matches.</p>
</abstract>

A comprehensive assessment of the physical capabilities and attributes of soccer players is essential for effective college soccer training. The ability to implement targeted tactical training relies on a thorough understanding of each player’s physical condition. This paper presents a monitoring system for soccer players’ bodily functions, developed using inertial sensor technology alongside Beer’s Law, tailored to recognize human body movement states and to measure blood oxygen and heart rate. The system’s efficacy in movement recognition and physiological monitoring was evaluated through comparative testing, assessing its performance against established algorithms. Notably, this system demonstrated a 9.75% improvement in the average accuracy for recognizing walking and running actions compared to the Juha Parkka decision tree algorithm and a 6.5% increase in accuracy over the D.M. Karantonis algorithm for walking action recognition. Heart rate measurements incurred an error of approximately ±2 BPM, with a maximum error of 1.7, and oxygen saturation monitoring displayed an error margin of ±2%, with a maximum deviation of 1.6. These deviations fall within acceptable limits. The results of this study furnish valuable data to support the customization of training content and intensity for college soccer athletes and offer insights for optimizing player selection and tactical strategies during matches.

Design of physical function monitoring system for athletes in soccer training in college sports

Shagnhhai Lixin University of Accounting and Finance

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A comprehensive assessment of the physical capabilities and attributes of soccer players is essential for effective college soccer training. The ability to...

Design of physical function monitoring system for athletes in soccer training in college sports

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 01 mar 2024

Accettato: 05 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1378

Parole chiave
Action recognition, Inertial sensor, Blood oxygen saturation, Heart rate monitoring, Soccer training

© 2024 Bin Ji et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Design of physical function monitoring system for athletes in soccer training in college sports

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 01 mar 2024

Accettato: 05 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1378

Parole chiaveAction recognition, Inertial sensor, Blood oxygen saturation, Heart rate monitoring, Soccer training

© 2024 Bin Ji et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Action recognition, Inertial sensor, Blood oxygen saturation, Heart rate monitoring, Soccer training