Body Sensor Network-Based Gait Quality Assessment for Clinical Decision-Support via Multi-Sensor Fusion

IEEE Access

Long Liu¹  Jiaxin Wang²  Sen Qiu²  Zhelong Wang²  Hongyu Zhao²  Jinxiao Li²  Shengming Li³  Kai Tang⁴ 

[1]Department of Electronic Engineering, Dalian Neusoft Institute of Information, Dalian, China

[2]Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China

[3]School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China

[4]The First Affiliated Hospital of Dalian Medical University, Dalian, China

关键词: Wearable computing;    inertial sensors;    feature-level fusion;    machine learning;   

DOI  :  10.1109/ACCESS.2019.2913897

来源: DOAJ

【 摘 要 】

This paper presents a versatile multi-sensor fusion method and decision-making algorithm for ambulatory and continuous patient monitoring purposes via a body sensor network (BSN). Gait features including spatio-temporal parameters, gait asymmetry, and regularity were identified and estimated from individual patients data collected from clinical trials. Hence, a continuous assessment and diagnosis of the improvement or the deterioration of the lower limb rehabilitation process is ensured. The experimental results from 10-m free walking trials indicated that the proposed method has a good consistency with the clinically used observational method. The gait assessment results were comparable with previous studies. Gait segmentation succeed even when the pace deviates significantly from the healthy subjects' reference value, which provides proof of objectivity and effectiveness of this preliminary research, namely, using wearable inertial measurement unit (IMUs) as an indicator to detect gait abnormality in subjects with neurological disorders. The hypothesis of gait quality-related clinical trials were designed and validated via both machine learning approach and feature layer data fusion. With further validations, the proposed inertial sensor-based gait assessment approach has the potential to be applied both routinely in clinical practice and for tele-health scenes such as fall detection of the elder at home.

【 授权许可】

Unknown