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 paper 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 kneeankle 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.

Although powered prosthetic legs have enabled more biomimetic joint kinematics during steady-state activities like walking and stair climbing, transitions between these activities are usually handled by discretely switching controllers without considering biomimicry or the distinct role of the leading leg. This study introduces two data-driven, phase-based kinematic control approaches for seamless inter-leg transitions (i.e., initiated by either the prosthetic or intact leg) between walking and stair ascent/descent, assuming high-level knowledge of the upcoming activity. One approach employs a novel continuously-varying kinematic model that interpolates between steady-state activities as an approximate convex combination, and the other approach employs a simple switching-based model with optimized switching timing and tunable smoothing of kinematic discontinuities. Data-driven analysis indicates the continuously-varying controller remains beneficial over the switching controller for a range of classification delays. Experimental validation with a powered knee-ankle prosthesis used by two high-functioning transfemoral amputees demonstrates the continuous controller can provide more biomimetic and uninterrupted kinematic trajectories for both joints during transitions, irrespective of the initiating leg. This research underscores the potential for enabling more natural locomotion for high-functioning prosthetic leg users.

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This paper presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.5% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.