Detecting abnormal patterns of daily activities for the elderly living alone

Tingzhi Zhao (1) , Hongbo Ni (1) , Xingshe Zhou (1) , Qiang Lin (1) , Daqing Zhang (2, 3, 4, 5) , Zhiwen Yu (1)

1 SCS-NPU - School of Computer Science - Northwestern Polytechnical University
2 ACMES-SAMOVAR - Architecture, Cloud continuum, formal Models, artificial intElligence and Services in distributed computing
3 TSP - RST - Département Réseaux et Services de Télécommunications
4 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
5 CNRS - Centre National de la Recherche Scientifique

In order to reduce the potential risks associated with physically and cognitively impaired ability of the elderly living alone, in this work, we develop an automated method that is able to detect abnormal patterns of the elderly's entering and exiting behaviors collected from simple sensors equipped in home-based setting. With spatiotemporal data left by the elderly when they carrying out daily activities, a Markov Chains Model (MCM) based method is proposed to classify abnormal sequences via analyzing the probability distribution of the spatiotemporal activity data. The experimental evaluation conducted on a 128-day activity data of an elderly user shows a high detection ratio of 92.80% for individual activity and of 92.539% for the sequence consisting of a series of activities

Mots clés: Abnormal pattern; Infrared tube; Spatiotemporal; MCM

Domaines: Traitement du signal et de l'image [eess.SP]

Tingzhi Zhao, Hongbo Ni, Xingshe Zhou, Qiang Lin, Daqing Zhang, et al.. Detecting abnormal patterns of daily activities for the elderly living alone. HIS 2014 : 3rd international conference on Health Information Science, Apr 2014, Shenzhen, China. pp.95 - 108, ⟨10.1007/978-3-319-06269-3_11⟩. ⟨hal-01298140⟩