Body Sensor Network-Based Robust Gait Analysis: Toward Clinical and at Home Use

Abstract

Gait analysis has become an important tool for diagnosing disease and evaluating disease progression. Currently gait analysis was mainly conducted by experienced physicians and relies on medical observation or complex medical equipment; hence, the application has been limited. This research aims to build an ambulatory gait analysis system based on the emerging body sensor networks. A calibration method for a magnetometer was proposed to deal with ubiquitous a magnetic disturbance. A proportional integral controller-based complementary filter and error correction of gait parameters have been defined with a multi-level data fusion algorithm. Preliminary gait analysis trials were conducted on both healthy subjects and patients with abnormal gait. Experimental results indicated that the proposed scheme has significant advantages in terms of test accuracy and efficiency, as well as operation complexity compared with conventional gait analysis approaches. Accordingly, measurement of gait abnormality may provide a wealth of information regarding neuromotor status and the characterization of some neurological disorders. It is concluded that the proposed gait analysis system has great potential as an auxiliary for medical rehabilitation.