Detection Of Gait Impairment In The Elderly Using Patch-Gei

We propose a novel method for estimating physical impairment of elderly people using gait. To achieve this, we first investigate which gait feature is effective for this purpose among gait energy image (GEI), duration time, and phase fluctuation as dynamic features. GEI is a popular appearance-based feature showing high performance in human authentication. By comparison, we find that it is the most reasonable feature. In real situations, however, GEI is easily affected by clothes variations or carrying conditions, so that the use of whole body results in decreasing performance. Considering this problem, we thus propose to use only the GEI features of the most discriminative body patches. From the experiments that evaluate the contribution of various sizes of body patches, we find that head and chest regions perform better than the whole body with the classification accuracy improved from 80.93% to 83.17% for the visual impairment discrimination case. As for the leg impairment detection case, the leg region performs better than the whole body by an accuracy increased from 69.30 to 75.05%. These results confirm the effectiveness of patch-GEI for impairment detection. (C) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.