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Title: A self-learning fall detection system for elderly persons using depth camera

Authors: Xiangbo Kong; Lin Meng; Hiroyuki Tomiyama

Addresses: Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan ' College of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan ' College of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan

Abstract: The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.

Keywords: healthcare; elderly persons; fall detection; self-learning; posture analysis; eye status; support vector machine; SVM.

DOI: 10.1504/IJAMECHS.2020.109898

International Journal of Advanced Mechatronic Systems, 2020 Vol.8 No.1, pp.16 - 25

Received: 08 May 2019
Accepted: 13 Nov 2019

Published online: 15 Sep 2020 *

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