Title: Early detection of a fall using Wi-Fi and deep learning
Authors: Warayut Surasakhon; Attaphongse Taparugssanagorn; Sarawut Lerspalungsanti; Khirakorn Thipprachak
Addresses: ICT Department, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, 58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathum Thani, 12120, Thailand ' ICT Department, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, 58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathum Thani, 12120, Thailand ' National Metal and Materials Technology Center (MTEC), Pathumthani, Thailand ' Department of Control System and Instrumentation Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Abstract: There have been millions of dramatic falls in the elderly population, the leading cause of traumatic injury and death. While the problem is more serious, the number of elderly is continuously increasing. To relieve this issue, early detection of a fall is very helpful for the elderly who are concerned about falling or a health problem when alone and can automatically alert medical responders. In this paper, we present a fall detection system using Wi-Fi channel state informations (CSIs). Wi-Fi technology is selected since it is ubiquitous allowing our system to work everywhere even in the private area like in a bathroom and to be easily implemented with low cost. The CSI provides information not only about the environment, but also human movements. To interpret an event as fall or non-fall, we employ several classification algorithms as well as deep learning models, which can provide very accurate results up to 97.7%.
Keywords: fall detection; activity recognition; channel state information; CSI; machine learning; deep learning.
International Journal of Sensor Networks, 2022 Vol.40 No.3, pp.190 - 202
Received: 30 Jan 2022
Accepted: 09 Jun 2022
Published online: 22 Nov 2022 *