Department of Mechanical Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA.
Electronics and Telecommunications Research Institute, ICT, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea.
Sensors (Basel). 2019 Nov 18;19(22):5024. doi: 10.3390/s19225024.
Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.
步态是指个体的行走模式。它可以是正常的，也可以是异常的，这取决于个体的健康状况。本文考虑开发一种步态传感器网络系统，该系统使用一对无线惯性测量单元 (IMU) 传感器来监测用户的步态周期。传感器信息用于确定腿部运动的正常性。传感器系统将 IMU 传感器放置在一条腿上，以提取行走时髋关节和膝关节的三维角运动。可穿戴传感器是圣地亚哥州立大学定制的，具有无线数据传输能力。该系统使用户能够在任何地点（包括非实验室环境）收集步态数据。本文还介绍了将一对 IMU 所经历的运动分解为个体和相对三个方向的髋关节和膝关节运动的数学计算方法。此外，还提出了一种基于髋关节和膝关节之间相位差角的新步态模式分类方法。实验结果表明，分类方法在智能检测异常步态模式等领域具有潜在的应用价值。
