Parametric extraction of spatiotemporal gait features using wireless foot sensor module.

This work reports the extraction and evaluation of clinically relevant spatiotemporal and statistical gait parameters from developed wireless foot sensor module as recommended by the Biomathics and Canadian Gait Consortium Initiative. Further, normalization of extracted spatiotemporal gait parameters reduces inter-subject physiological variations. To validate their performance towards gait analysis, a machine learning framework is implemented for personnel identification. The study results suggest a promising potential for utilizing the extracted feature-set for the automatic multiclass gait disorders classification. Developed module is a low cost, easy-to-use device, and has potential application for setups with limited access to state of art gait analysis laboratory.