TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Asadov, Akhmadbek
A1  - Ortega, Juan Antonio
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
T1  - Assessing Body Position During Sleep Using FSR Sensors and Machine Learning Algorithms
JF  - Models and Applications for Embedded Systems
N2  - This study investigates the application of Force Sensing Resistor (FSR) sensors and machine learning algorithms for non-invasive body position monitoring during sleep. Although reliable, traditional methods like Polysomnography (PSG) are invasive and unsuited for extended home-based monitoring. Our approach utilizes FSR sensors placed beneath the mattress to detect body positions effectively. We employed machine learning techniques, specifically Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost algorithms, to analyze the sensor data. The models were trained and tested using data from a controlled study with 15 subjects assuming various sleep positions. The performance of these models was evaluated based on accuracy and confusion matrices. The results indicate XGBoost as the most effective model for this application, followed by RF and KNN, offering promising avenues for home-based sleep monitoring systems.
KW  - Body Position
KW  - FSR Sensors
KW  - Machine Learning Algorithms
Y1  - 2023
UR  - www.dii.univpm.it/MAES-2023
SN  - 978-88-87548-00-6
SB  - 978-88-87548-00-6
SP  - 19
EP  - 22
PB  - Università Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione
CY  - Ancona, Italy
ER  -