MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion.

Sen Qiu,Long Liu,Hongyu Zhao,Yongmei Jiang,Zhelong Wang

doi:10.3390/mi9090442

Abstract

Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

Highlights

Human walking contains important physiology, kinematic and dynamic information
The results from statistical results presented in the table indicate strong evidence of the capability of the typical gait parameters in characterizing the walk of healthy subjects and patients
This paper aims to provide ambulatory and robust measurements of human gait, and we adopted body-worn sensors to estimate gait parameters

Summary

Introduction

Human walking contains important physiology, kinematic and dynamic information. There are many application prospects of human gait analysis in real life, such as monitoring the patient’s recovery progress in clinical practice, the control strategy of bionic robots, etc. Stroke is a common neurodegenerative condition with a principal symptom of progressive limbs movement disorder The prevalence of this neurological disease has been estimated at 120 affected individuals per 100,000 and 75% of the victims suffer from sequelae (mostly involve gait dysfunction) afterwards according to epidemiological statistics [1,2,3]. Objective and accurate inspection of gait parameters (as shown in Table 1) is of great help to a neurologist for appropriate assessment and diagnosis of stroke patients. To this end, the findings of this study could be helpful for revealing the pathogenesis of gait disorders. It is crucial to discover the main components of gait disorders

Objectives

Methods

Results

Conclusion