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Open AccessArticle

Robust Self-Adaptation Fall-Detection System Based on Camera Height

1
Graduate School of Science and Engineering, Ritsumeikan University, Kyoto 525-8577, Japan
2
Department of Electronic and Computer Engineering, College of Science and Engineering, Ritsumeikan University, Kyoto 525-8577, Japan
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(17), 3768; https://doi.org/10.3390/s19173768
Received: 21 July 2019 / Revised: 28 August 2019 / Accepted: 29 August 2019 / Published: 30 August 2019
(This article belongs to the Section Internet of Things)
Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods. View Full-Text
Keywords: fall detection; self-adaptation; camera height; practical fall detection; self-adaptation; camera height; practical
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MDPI and ACS Style

Kong, X.; Chen, L.; Wang, Z.; Chen, Y.; Meng, L.; Tomiyama, H. Robust Self-Adaptation Fall-Detection System Based on Camera Height. Sensors 2019, 19, 3768.

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