Indoor Sports

Behar, Alex; Hutchinson, Mark R.; Bobko, Aimee; Mayo, Benjamin C.; Schartzman, Garrett; Secretov, Erwin; Steffes, Matthew J.; Tayne, Samantha

doi:10.1007/978-3-030-10433-7_39

Cited by 1 publication

(1 citation statement)

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“…Consequently, they can be used to continuously collect biomechanical and physiological performance data to prevent significant adverse effects (sometimes even death) and increase the team’s efficiency by maintaining the players in a virtually optimal physical condition. In active indoor sports [ 5 ] games such as ice hockey, handball, and basketball, the players may be more or less tired, exhausted, and losing water (causing dehydration [ 6 , 7 ], which is also a cause of hypernatremia) depending on the game velocity and intensity level, as well as the surrounding ecosystem, including humidity/temperature conditions [ 8 ]. In addition, players’ performance fluctuates from game to game based on the time, location, and field position.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

Biró

Szilagyi

Szilágyi

et al. 2023

Sensors

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Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

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