Gait analysis for physical rehabilitation via body-worn sensors and multi-information fusion

Abstract

How to effectively use wearable sensors for medical rehabilitation is an interdisciplinary research hotspot of control subjects and biomedical engineering. This paper intends to integrate accelerometer, gyroscope and magnetometer to build a low-cost, intelligent and lightweight wearable human gait analysis platform. On account of complexity and polytopes of walking motion characteristics, the key is to solve the existing robustness and adaptability problems of current gait analysis algorithm. This project is starting from the sensor physical properties and human physiology structure, aiming to establish lower limb kinematics model constraint, and solving the applicability problem of the traditional zero velocity update algorithm. Digital filter and error correction of gait parameters could be done with multi-level data fusion algorithm. Preliminary clinical gait experiments results indicated the proposed method has great potential as an auxiliary for medical rehabilitation. The ultimate target is to realize auxiliary diagnosis and exercise rehabilitation plan formulation for patients with abnormal gait.