Gait analysis algorithm for lower limb rehabilitation robot applications

Abstract

When patients with lower limb dyskinesia use robots for rehabilitation training, gait parameters are of great significance for disease diagnosis and rehabilitation evaluation. Gait measurement is usually carried out by using optical motion capture systems, pressure plates and so on. However, it is difficult to apply these systems to lower limb rehabilitation robots due to their high price, limited scope and wearing requirements. At the same time, most of the current applications in robots focus on the basic gait parameters (such as step length and step speed) for robot control or user intention recognition. Therefore, this paper proposes an online gait analysis algorithm for lower limb rehabilitation robots, which uses a lidar sensor as the gait data acquisition sensor. The device is installed on the lower limb rehabilitation robot, which not only avoids the problems of decline in the detection accuracy and failure of leg tracking caused by lidar placement on the ground, but it also calculates seven gait parameters, such as step length, stride length, gait cycle and stance time, with high precision in real time. At the same time, the walking track of the patient may not be straight, and the lidar coordinate system is also changed due to the movement of the lower limb rehabilitation robot when the patient moves forward. In order to overcome this situation, a spatial parameter-splicing algorithm based on a time series is proposed to effectively reduce the error impact on gait spatiotemporal parameters. The experimental results show that the gait analysis algorithm proposed in this paper can measure the gait parameters effectively and accurately. Except for the swing time and double support time, which are calculated with large relative errors due to their small values, the relative errors of the remaining gait parameters are kept below 8 %, meeting the requirements of clinical applications.