Deep learning enabled fall detection exploiting gait analysis

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Abstract

Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434mathrm{x}2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Deep learning; Elderly fall; Gait analysis; LSTM; Pose estimation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	28 Nov 2023 05:05
Last Modified:	28 Nov 2023 05:05
URI:	http://umpir.ump.edu.my/id/eprint/39412
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