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Development of a user-adaptable human fall detection based on fall risk levels using depth sensor

Nizam, Yoosuf and Mohd, Mohd Norzali and Abdul Jamil, M. Mahadi (2018) Development of a user-adaptable human fall detection based on fall risk levels using depth sensor. Sensors, 18 (2260). pp. 1-14. ISSN 14248220

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Official URL: doi:10.3390/s18072260

Abstract

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

Item Type: Article
Uncontrolled Keywords: Falls; human fall; assistive living; daily activities; fall risk level
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electronic Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 23 Jun 2019 06:53
Last Modified: 23 Jun 2019 06:53
URI: http://eprints.uthm.edu.my/id/eprint/11562
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