NDLI: Statistical and Machine Learning Approach to Assessing the Environmental Impact on Walking Patterns

Content Provider	IEEE Xplore Digital Library
Author	Yang, Mingjing Zheng, Huiru Wang, Haiying McClean, Sally Mayagoitia, Ruth E.
Copyright Year	2014
Abstract	It has been recongised that subjects may change their gait pattern when walking in different environments. This paper investigated the impact of walking environments on gait monitoring and analysis. A tri-axial accelerometer attached to subject's lower back was used to record gait pattern while walking in 5 different urban environments (quiet street, busy street, cobbled street, dark street and checkerboard floor). Forty-one young students participated the experiment. For each trial, a total of 33 gait features were extracted, of which 11 were derived from the entire walking trial and 22 were computed for each stride cycle. Statistics analysis showed that 7 out of 11 features extracted from each trial were significantly different across the five environments. The obtained results suggested that different environments have various impacts on gait features extracted from accelerometer data. To further access the impact, a multi-layer perceptrons based hierarchical classification approach was proposed to discriminate stride cycles taken from different walking environments. The classification accuracy of each level ranged from 98.26% to 65.62% with the discrimination of walking in quiet environment achieving the best performance.
Starting Page	231
Ending Page	235
File Size	713142
Page Count	5
File Format	PDF
e-ISBN	9781479942749
DOI	10.1109/ICDMW.2014.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	urban walking Conferences accelerometer Gait pattern Data mining Acceleration classification
Content Type	Text
Resource Type	Article

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