An automatic in-home fall detection system using Doppler radar signatures

Issue title: Human-centric computing and intelligent environments

Guest editors: Gordon Hunter, Tiina Kymäläinen and Raúl Herrera-Acuña

Article type: Research Article

Authors: Liu, Liang^{a; *} | Popescu, Mihail^b | Skubic, Marjorie^a | Rantz, Marilyn^c | Cuddihy, Paul^d

Affiliations: [a] Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO, USA | [b] Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA | [c] Sinclair School of Nursing, University of Missouri, Columbia, MO, USA | [d] General Electric Global Research, Niskayuna, NY, USA

Correspondence: [*] Corresponding author. E-mail: llpfd@mail.missouri.edu.

Abstract: One in three elders over the age of 65 falls each year in the United States. This paper describes a non-invasive fall detection system based on a Doppler radar sensor. The developed system has been tested in two environments: laboratory and real senior living apartments. While some laboratory results appeared in our previous papers, the main novelty of this paper consists in the deployment of our fall detection system in six apartments from TigerPlace (a senior living facility in Columbia, Missouri). The fall detection results obtained in our laboratory were excellent, with the radar placed on the ceiling performing better than on the floor. The fall detection system was then evaluated using radar data collected over two weeks in six TigerPlace apartments. The fall detection system successfully detected all six natural senior falls in an apartment for the examined one week.

Keywords: Doppler radar, eldercare, fall detection, MFCC

DOI: 10.3233/AIS-160388

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 8, no. 4, pp. 453-466, 2016