Vision-Based Gait Events Detection Using Deep Convolutional Neural Networks

Authors
앙크자야 잠스란도르Nguyen, Mau DungPARK, MI NAKumar, Konki SravanMun, Kyung-RyoulKim, Jinwook
Issue Date
2021-11-01
Publisher
IEEE
Citation
43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), pp.1936 - 1941
Abstract
Accurate gait events detection from the video would be a challenging problem. However, most vision-based methods for gait event detection highly rely on gait features that are estimated using gait silhouettes and human pose information for accurate gait data acquisition. This paper presented an accurate, multi-view approach with deep convolutional neural networks for efficient and practical gait event detection without requiring additional gait feature engineering. Especially, we aimed to detect gait events from frontal views as well as lateral views. We conducted the experiments with four different deep CNN models on our own dataset that includes three different walking actions from 11 healthy participants. Models took 9 subsequence frames stacking together as inputs, while outputs of models were probability vectors of gait events: toe-off and heel-strike for each frame. The deep CNN models trained only with video frames enabled to detect gait events with 93% or higher accuracy while the user is walking straight and walking around on both frontal and lateral views.
ISSN
1557-170X
URI
https://pubs.kist.re.kr/handle/201004/77297
DOI
10.1109/EMBC46164.2021.9630431
Appears in Collections:
KIST Conference Paper > 2021
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