Analysis of a person's movement provides important information about his or her health status. This analysis can be performed with wearable devices or with contactless technologies. These latter in particular are of some interest, since the subject is free to move and the analysis of the movement is realistic. Despite being designed for other purposes, automotive mmWaves radars represent a powerful low-cost technology for detecting people's movements without contact which finds interesting applications as a support for home monitoring of health conditions. In this paper it is shown how to exploit commercial radars to distinguish with high precision the way of walking of a subject and the position of his hands during the activity carried out. The application of Principal Component Analysis (PCA) for feature extraction from raw data is considered, together with supervised machine learning algorithms for the actual classification of the various activities carried out during the experiments.
Contactless Walking Recognition based on mmWave RADAR / Senigagliesi, Linda; Ciattaglia, Gianluca; Gambi, Ennio. - ELETTRONICO. - (2020). (Intervento presentato al convegno 2020 IEEE Symposium on Computers and Communications tenutosi a Rennes; France nel 7 - 10 July 2020) [10.1109/ISCC50000.2020.9219565].
Contactless Walking Recognition based on mmWave RADAR
Linda Senigagliesi
;Gianluca Ciattaglia;Ennio Gambi
2020-01-01
Abstract
Analysis of a person's movement provides important information about his or her health status. This analysis can be performed with wearable devices or with contactless technologies. These latter in particular are of some interest, since the subject is free to move and the analysis of the movement is realistic. Despite being designed for other purposes, automotive mmWaves radars represent a powerful low-cost technology for detecting people's movements without contact which finds interesting applications as a support for home monitoring of health conditions. In this paper it is shown how to exploit commercial radars to distinguish with high precision the way of walking of a subject and the position of his hands during the activity carried out. The application of Principal Component Analysis (PCA) for feature extraction from raw data is considered, together with supervised machine learning algorithms for the actual classification of the various activities carried out during the experiments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.