Detecting Toe-Off Events Utilizing a Vision-Based Method

Clicks: 103
ID: 118837
2019
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Abstract
Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.
Reference Key
tang2019entropydetecting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yunqi Tang;Zhuorong Li;Huawei Tian;Jianwei Ding;Bingxian Lin;Tang, Yunqi;Li, Zhuorong;Tian, Huawei;Ding, Jianwei;Lin, Bingxian;
Journal entropy
Year 2019
DOI
10.3390/e21040329
URL
https://www.mdpi.com/1099-4300/21/4/329
Keywords
Convolutional neural network toe-off detection gait event silhouettes difference

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