Detecting heel strikes for gait analysis through acceleration flow

In some forms of gait analysis, it is important to be able to capture when the heel strikes occur. In addition, in terms of video analysis of gait, it is important to be able to localise the heel where it strikes on the floor. In this study, a new motion descriptor, acceleration flow, is introduced...

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Bibliographic Details
Published in:	IET computer vision 2018-08, Vol.12 (5), p.686-692
Main Authors:	Sun, Yan, Hare, Jonathon S, Nixon, Mark S
Format:	Article
Language:	eng
Subjects:	acceleration flow detector acceleration flow quantity closed circuit television gait analysis Gaussian noise Gaussian white noise heel strikes detection image motion analysis key frame motion descriptor multiple views object detection real CCTV footage region-of-interest Research Article spatial positioning temporal detection video signal processing walking directions white noise
Online Access:	Request full text
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Summary:	In some forms of gait analysis, it is important to be able to capture when the heel strikes occur. In addition, in terms of video analysis of gait, it is important to be able to localise the heel where it strikes on the floor. In this study, a new motion descriptor, acceleration flow, is introduced for detecting heel strikes. The key frame of heel strike can be determined by the quantity of acceleration flow within the region of interest, and positions of the strike can be found from the centre of rotation caused by radial acceleration. Our approach has been tested on a number of databases which were recorded indoors and outdoors with multiple views and walking directions for evaluating the detection rate under various environments. Experiments show the ability of our approach for both temporal detection and spatial positioning. The immunity of this new approach to three anticipated types of noises in real CCTV footage is also evaluated in our experiments. The authors acceleration flow detector is shown to be less sensitive to Gaussian white noise, whilst being effective with images of low-resolution and with incomplete body position information when compared with other techniques.
ISSN:	1751-9632 1751-9640 1751-9640