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.