Improving Automatic Control of an Ankle-Foot Prosthesis Using Machine Learning Algorithms

Abstract

Most commercially available lower-limb prostheses are designed for walking, not for standing. The Minneapolis VA Health Care System has developed a bimodal prosthetic ankle-foot system with distinct modes for walking and standing [1]. With this device, a prosthesis user can select standing or walking mode in order to maximize standing stability or walking functionality, depending on the activity and context. Additionally, the prosthesis was designed to allow for an “automatic mode” to switch between standing and walking modes based on readings from an onboard Inertial Measurement Unit (IMU) without requiring user interaction to manually switch modes. A smartphone app was also developed to facilitate changing between walking, standing and automatic modes. The prosthesis described in [1] was used in a pilot study with 18 Veterans with lower-limb amputations to test static, dynamic, and functional postural stability. As part of the study, 17 Veterans were asked for qualitative feedback on the bimodal ankle-foot system (Table 1). The majority of participants (82%) expressed an interest in having an automatic mode. The participants also indicated that the automatic mode would need to reach walking mode on their first step and to lock the ankle quickly once the standing position was achieved. When asked about how they wanted to control the modes of the prosthesis, 82% wanted to use a physical switch and only 12% wanted to use a smartphone app. The results indicated that the following major design changes would be needed: 1) A fast and accurate automatic mode 2) A physical switch for mode changes This paper describes the use of machine learning algorithms to create an improved automatic mode and the use of stakeholder feedback to design a physical switch for the bimodal ankle-foot system.