Human Gait Analysis using Hidden Markov Model Technique

Abstract

This research presents a novel approach to human gait analysis using wearable Inertial Measurement Unit(IMU) sensor-based technique.The proposed system emphasizes on
detection of certain abnormal gait pattern. It includes hemiplegic and equinas gait which are synthetically generated in our lab.The designed prototype contains an IMU sensor with 3 axial accelarometer and gyroscope. It provides linear accelaration and angular velocity of human foot.A probabilistic framework, Hidden Markov Model(HMM) is applied to model bipedal human gait.This model uses Symbolic Aggregate Approximation(SAX) method for generating observation sequences obtained from sample gait cycles.The detection of abnormal gait pattern is based on maximum log-likelihood of an unknownobserverd sequence,generated from a gait cycle.The experimental results demonstarte that the proposed HMM-based technique is able to detect gait abnormality in gait data.The proposed personalized gait modelling approach is cost effective and reliable to implement in gait rehabilatation process.