Indexed by:Journal Papers

Date of Publication:2020-02-01

Journal:SENSORS

Included Journals:EI、PubMed、SCIE

Volume:20

Issue:4

Key Words:MEMS sensors; information fusion; gait analysis; rehabilitation assessment; body sensor network

Abstract:Human gait reflects health condition and is widely adopted as a diagnostic basis in clinical practice. This research adopts compact inertial sensor nodes to monitor the function of human lower limbs, which implies the most fundamental locomotion ability. The proposed wearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy. It can output the kinematic parameters of joint flexion and extension, as well as the displacement data of human limbs. The experimental results provide strong support for quick access to accurate human gait data. This paper aims to provide a clue for how to learn more about gait posture and how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database, it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injury risks, and chronic pain, and provides guidance for arranging personalized rehabilitation programs for patients. The proposed framework may eventually become a useful tool for continually monitoring spatio-temporal gait parameters and decision-making in an ambulatory environment.