Ambilateral Activity Recognition and Continuous Adaptation with a Powered Knee-Ankle Prosthesis

For powered prosthetic legs to be viable in everyday situations, they require an activity classification system that is not only accurate but also straightforward to understand and use. However, incorporating the numerous activity modes in real-world ambulation often requires high-dimensional feature spaces and restrictions on the leg leading each transition. This article addresses these challenges by delegating sit/stand transitions and variable-incline walking to the mid-level controller, effectively reducing the classification space to four states with easily distinguishable features. We implement simple heuristic rules for both prosthetic-led and intact-led (i.e., ambilateral) transitions, using lower-limb kinematic features, ground contact and inclination, and environmental distance from an ultrasonic sensor. Two transfemoral amputee subjects using a powered knee-ankle prosthesis demonstrated an ambilateral transition accuracy of 99.2% under both self-paced and rapid-paced/fatiguing conditions, with a 100% recovery rate due to backup logic or user-cued resets. The incline estimator enabled the prosthesis to continuously adapt between level and inclined surfaces without explicit classification. These results and an outdoor multi-terrain demonstration indicate that simple and straightforward transition logic can enable powered prosthetic legs to be used reliably across a broad array of daily activities.