Detection of Gait Phases Using Orient Specks for Mobile Clinical Gait Analysis

This paper presents a hybrid method based on a feed-forward neural network (FNN) embedded in a hidden Markov model (HMM), for detecting phases in a gait cycle, based on data from inertial sensors attached to the lower body. The method was validated against the ground truth obtained concurrently from a Vicon optical motion capture system for five volunteers. The method was characterised using metrics such as sensitivity and specificity for sensor placements, and gait analysis. The results demonstrate that the proposed method is accurate within 23 milliseconds with the added advantages of mobility afforded by wireless sensors and the flexibility of the classification method.