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 impe-dance 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 levelground 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.

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Modelling of human motion is used in a wide range of applications. An important aspect of accurate representation of human movement is the ability to customize models to account for individual differences. The following work proposes a methodology using Hill-based candidate functions in the Fast Orthogonal Search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force estimation framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface EMG data from three muscles of the upper arm (biceps brachii, brachioradialis and triceps brachii) were recorded from 10 subjects as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint kinematic data (joint angle and angular velocity) were utilized as inputs to the FOS model. The resulting wrist force estimations were found to be more accurate for models utilizing Hill-based candidate functions, than models utilizing candidate functions that were not physiologically relevant. Subject-speci¯c estimates of optimal joint angle were determined via frequency analysis of the selected FOS candidate functions. Subject-specfic optimal joint angle estimates demonstrated low variability and fell within the range of angles presented in the literature.

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