Gait Event Prediction of People with Cerebral Palsy using Feature Uncertainty: A Low-Cost Approach

Abstract

Incorporation of feature uncertainty during model construction explores the real generalization ability of that model. But this factor has been avoided often during automatic gait event detection for Cerebral Palsy patients. Again, the prevailing vision-based gait event detection systems are expensive due to incorporation of high-end motion tracking cameras. This study proposes a low-cost gait event detection system for heel strike and toe-of events. A state-space model was constructed where the temporal evolution of gait signal was devised by quantifying feature uncertainty. The model was trained using Cardif classifer. Ankle velocity was taken as the input feature. The frame associated with state transition was marked as a gait event. The model was tested on 15 Cerebral Palsy patients and 15 normal subjects. Data acquisition was performed using low-cost Kinect cameras. The model identifed gait events on an average of 2 frame error. All events were predicted before the actual occurrence. Error for toe-of was ≈ 21% less than the heel strike. Incorporation of the uncertainty factor in the detection of gait events exhibited a competing performance with respect to state-of-the-art.