Biomedical Radar System for Real-Time Contactless Fall Detection and Indoor Localization

• Published in: IEEE journal of electromagnetics, RF and microwaves in medicine and biology Vol. 7; no. 4; pp. 1 - 10
• Main Authors: Mercuri, Marco, Soh, Ping Jack, Mehrjouseresht, Pouya, Crupi, Felice, Schreurs, Dominique
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Base stations; Contactless; Data processing; Fall detection; Localization; Location awareness; Monitoring; movement classification; Public health; Radar; Radar antennas; Radar equipment; radar remote sensing; Radar systems; Real time; real-time health monitoring; Real-time systems; Root-mean-square errors; Support vector machines; tagless localization; Wireless communications; zigbee communication
• ISSN: 2469-7249; 2469-7257
• DOI: 10.1109/JERM.2023.3278473

Fall incidents represent a major public health problem among elderly people. This resulted in a significant increase of the number of investigated systems aiming at detecting falls promptly. In this respect, in this work, a biomedical radar system is proposed for remote real-time fall detection and indoor localization. The system, consisting of a sensor and a base station, combines radar and wireless communication techniques, and uses a data processing technique to distinguish between fall events and normal movements. The classification, based on a Least-Square Support Vector Machine (LS -SVM), combined with the sliding window principle allows to perform fall detection in real-time. Moreover, it is capable to localize the subjects when the fall incident has been detected. The in-vivo validation showed a high success rate in detecting fall events, with a maximum delay of 340 ms. Moreover, a maximum mean absolute errors (MAE) of 3.8 cm and a maximum root-mean-square error (RMSE) of 7.5 cm were reported in measuring the subject's absolute distance.