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Sensors 2018, 18(7), 2260; https://doi.org/10.3390/s18072260

Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

1
Biomedical Engineering Modeling and Simulation (BIOMEMS) Research Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
2
Embedded Computing Systems (EmbCos) Research Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
3
Computer Signal, Imaging and Intelligent (CSII) Research Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
*
Author to whom correspondence should be addressed.
Received: 19 April 2018 / Revised: 23 May 2018 / Accepted: 6 June 2018 / Published: 13 July 2018
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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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. View Full-Text
Keywords: falls; human fall; assistive living; daily activities; fall risk level falls; human fall; assistive living; daily activities; fall risk level
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Nizam, Y.; Mohd, M.N.H.; Jamil, M.M.A. Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor. Sensors 2018, 18, 2260.

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