Estimation of Gait Parameters based on Motion Sensor Data

Kaitai Li, Congcong Zhou
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引用次数: 2

Abstract

: Recently, the spreading application of intelligent mobile devices with integrated sensors such as inertial measurement units (IMU) has attracted the interest of the researchers for designing gait analysis methods based on the captured sensor data. This paper focuses on designing a system which can evaluate the walking ability and the physical agility level of normal people and people with Parkinson’s disease or stroke. The motion signal is collected by three wearable MPU9250 sensors located on both ankles and the center of the waist. Three test scenarios, including 10 meters walking test (10MWT), Time up and go test (TUGT) and Dual-task walking (DTW), are designed in this paper. The results, which concluded time parameters such as standing up time and turning back time as well as walking parameters such as stride length and stride frequency, showed good consistency and high accuracy with Vicon device.
基于运动传感器数据的步态参数估计
近年来,惯性测量单元(IMU)等集成传感器的智能移动设备的广泛应用引起了研究人员对基于传感器捕获数据设计步态分析方法的兴趣。本文旨在设计一套能够对正常人和帕金森病患者或脑卒中患者的行走能力和身体敏捷性水平进行评估的系统。运动信号由三个可穿戴的MPU9250传感器收集,这些传感器分别位于脚踝和腰部中心。本文设计了10米步行测试(10MWT)、Time up and go测试(TUGT)和双任务步行(DTW)三种测试场景。结果表明,Vicon装置测量的站立时间、转身时间等时间参数和步幅、步频等步行参数具有较好的一致性和较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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