Real-Time Estimation of Walking Speed and Stride Length Using an IMU Embedded in a Robotic Hip Exoskeleton

Gait parameters, including walking speed and stride length, are crucial indicators of health status and rehabilitation progress for individuals using wearable robots for exercise or rehabilitation. These metrics play a crucial role in monitoring progress and adjusting training programs, thereby fostering greater engagement in the training. In this paper, we present methods for estimating walking speed and stride length using sensors in wearable hip exoskeleton GEMS-H. Our study collected data from 79 middle-aged healthy individuals walking on a treadmill while wearing GEMS-H under various assistance conditions. To estimate walking speed, we evaluated linear regression models, deep neural networks, and ensemble models using different combinations of joint encoders and an IMU in the GEMS-H hip exoskeleton to form various sets of features. The ensemble of deep neural networks using only 6-DOF IMU signals as features achieved the lowest root-mean-square error (RMSE) for walking speed estimation, which was 0.066 m/s. We also present an algorithm for real-time stride length estimation, building on one of the speed estimation models. The speed and stride length estimation model was tested on 12 middle-aged healthy subjects walking in GEMS-H overground, yielding an RMSE of 0.060 m/s for speed and 7.1 cm for stride length.