RT Journal Article
T1 Machine learning on prediction of relative physical activity intensity using medical radar sensor and 3D accelerometer
A1 Biró, Attila
A1 Szilágyi, Sándor Miklós
A1 Szilágyi, László
A1 Martín-Martín, Jaime
A1 Cuesta-Vargas, Antonio
K1 Fatiga (Fisiología) -- Medición
K1 Deportes -- Aspectos fisiológicos -- Medición
K1 Medicina deportiva
K1 Deportes -- Fisioterapia
AB 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).  [...]
PB MDPI
YR 2023
FD 2023-03-30
LK https://hdl.handle.net/10630/26804
UL https://hdl.handle.net/10630/26804
LA eng
NO Biró A, Szilágyi SM, Szilágyi L, Martín-Martín J, Cuesta-Vargas AI. Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. Sensors. 2023; 23(7):3595. https://doi.org/10.3390/s23073595
NO Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026