Indexed by:期刊论文

Date of Publication:2019-01-01

Journal:IEEE ACCESS

Included Journals:SCIE、EI

Volume:7

Page Number:59884-59894

ISSN No.:2169-3536

Key Words:Wearable computing; inertial sensors; feature-level fusion; machine learning

Abstract: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.