Evaluation of Continuous Walking Speed Determination Algorithms and Embedded Sensors for a Powered Knee & Ankle Prosthesis

Dynamically altering the parameters for assistance in a lower limb prosthesis is a challenge that depends directly on the ability to estimate gait parameters. Machine learning algorithms present an opportunity to develop methods for continuously determining walking speed in different conditions. Current state-of-the-art solutions involve using wearable sensors such as IMUs to estimate these parameters. These methods require an entire gait cycle to update the walking speed; this leads to delays when responding to changing speeds and ultimately renders these methods ineffective for adaptation into real-time prosthesis control. In this study, we developed subject dependent and independent machine learning models for rapidly determining walking speed and evaluated on data collected from 6 individuals with unilateral transfemoral amputation walking on our robotic knee/ankle prosthesis. We evaluated the performance of these models across a variety of static walking speeds and dynamic speed trials. Our findings suggest that using machine learning models offers excellent accuracy for both subject dependent and subject independent algorithms (DEP RMSE: 0.014 ± 0.001 m/s, IND RMSE: 0.070 ± 0.007 m/s, (p <; 0.05), with the advantage of real-time continuous determination at 50 Hz, which allows for good performance when rapidly changing walking speed. We also determine the most effective sensors to use for improving model performance. Our study provides valuable information for determining walking speed more reliably across different users and is robust to dynamic changes experienced in gait.