Sensor-based detection of abnormal events for elderly people using deep belief networks. (28th January 2020)

• Record Type: Journal Article
• Authors: Huang, Yo-Ping; Basanta, Haobijam; Kuo, Hung-Chou; Chiao, Hsin-Ta
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 33:Number 1(2020)
• Page Start: 36
• Page End: 47
• Publication Date: 2020-01-28
• Subjects: sensors -- deep belief network -- DBN -- daily activities -- abnormal events; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Various technological developments in home-care systems have allowed elderly people to live independently without compromising their safety. A pilot study employing deep learning algorithm was conducted to study the daily routines of elderly people. We monitored unsupervised, diverse daily activities of elderly people such as household chores, sleeping, cooking, cleaning, using the bathroom, watching television, and meditating. The activities were monitored to track human-environment interactions by using motion sensors, actuators, and surveillance systems that were mounted inside living rooms, bedrooms, and kitchens and on bathroom doorways to detect safety hazards in the environment for elderly people. Such collected data were used in deep belief networks to ascertain and identify activities that are related to various health and self-care problems. Simulation results show that the proposed system outperforms the support vector machines in terms of F1 score and accuracy in identifying daily activities.