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

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.