Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits

The human ankle varies impedance and delivers net positive work during the stance period of walking. In contrast, commercially available ankle-foot prostheses are passive during stance, causing many clinical problems for transtibial amputees, including non-symmetric gait patterns, higher gait metabolism, and poorer shock absorption. In this investigation, we develop and evaluate a myoelectric-driven, finite state controller for a powered ankle-foot prosthesis that modulates both impedance and power output during stance. The system employs both sensory inputs measured local to the external prosthesis, and myoelectric inputs measured from residual limb muscles. Using local prosthetic sensing, we first develop two finite state controllers to produce biomimetic movement patterns for level-ground and stair-descent gaits. We then employ myoelectric signals as control commands to manage the transition between these finite state controllers. To transition from level-ground to stairs, the amputee flexes the gastrocnemius muscle, triggering the prosthetic ankle to plantar flex at terminal swing, and initiating the stair-descent state machine algorithm. To transition back to level-ground walking, the amputee flexes the tibialis anterior muscle, triggering the ankle to remain dorsiflexed at terminal swing, and initiating the level-ground state machine algorithm. As a preliminary evaluation of clinical efficacy, we test the device on a transtibial amputee with both the proposed controller and a conventional passive-elastic control. We find that the amputee can robustly transition between the finite state controllers through direct muscle activation, allowing rapid transitioning from level-ground to stair walking patterns. Additionally, we find that the proposed finite state controllers result in a more biomimetic ankle response, producing net propulsive work during level-ground walking and greater shock absorption during stair descent. The results of this study highlight the potential of prosthetic leg controllers that exploit neural signals to trigger terrain-appropriate, local prosthetic leg behaviors.