Development and Online Validation of an Intrinsic Fault Detector for a Powered Robotic Knee Prosthesis

Abstract

Robotic prosthetic legs have the potential to significantly improve the quality of life for lower limb amputees to perform locomotion in various environments and task conditions. However, these devices lack the capability to recover from internal intrinsic control faults, which can lead to harmful consequences affecting the user's gait performance and eroding trust in these robotic devices. Therefore, a reliable fault detection system is necessary to detect intrinsic faults in a timely manner and provide a compensatory response to mitigate their effects. This paper focuses on designing an active fault detector for a robotic knee prosthesis and demonstrates its effectiveness in real time. The developed system utilizes a Gaussian Process model to estimate knee angular velocity, which is sensitive to intrinsic faults and relies on the difference between estimated velocity and the actual measurement to detect internal control faults. In an offline analysis, the developed detector demonstrated a higher detection rate, lower false alarm ratio, and faster detection time compared with the two approaches reported previously. An online demonstration was also conducted with a unilateral amputee participant and showed performance similar to that of offline analysis. We expect that this detector can be integrated into a fault tolerance strategy to enhance the reliability and safety of robotic prosthetic legs, enabling users to perform their everyday tasks with greater confidence.