Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian University of Technology, Dalian 116024, China.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2020 Feb 21;20(4):1193. doi: 10.3390/s20041193.
Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable 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 displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand 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 injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.
人类步态反映了健康状况,被广泛应用于临床实践中的诊断基础。本研究采用紧凑的惯性传感器节点来监测人体下肢的功能,这暗示了最基本的运动能力。所提出的可穿戴步态分析系统能够精确地捕捉肢体运动并重建 3D 模型,输出关节屈伸的运动学参数以及肢体的位移数据。实验结果为快速获取准确的人体步态数据提供了有力支持。本文旨在为了解步态姿势以及可穿戴步态分析如何增强临床效果提供线索。随着步态数据库的不断扩展,它有可能帮助物理治疗师快速发现异常步态、运动损伤风险和慢性疼痛的原因,并为患者安排个性化康复计划提供指导。所提出的框架最终可能成为在非卧床环境中持续监测时空步态参数和决策的有用工具。