Estimating the Relationship between Multivariable Standing Ankle Impedance and Lower Extremity Muscle Activation
© 2018 IEEE. This study investigated artificial neural networks (ANN) that quantified the loaded ankle impedance of healthy subjects as they contracted their lower extremity muscles. The multivariable standing ankle impedance of 12 male subjects was determined using an instrumented vibrating platform. Surface electromyography (EMG) was used to measure the muscle activity while the subject's muscles were relaxed, and co-contracted to 10%, 20%, 30%, and 40% of the subject's maximum voluntary contraction (MVC). The function fitting capabilities of ANN were used to relate the input information (measured EMG and subject biometrics) to the desired output ankle impedance parameters (stiffness, damping, and inertia). The results showed that the relationship between muscle activity and standing ankle impedance can be modeled with high accuracy, and showed feasibility towards a generalized model that works for subjects that did not participate in the experiment. Using the predicted impedance, future work could investigate this relationship during walking and be used to advance active ankle-foot prostheses control based on the user's intentions.
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Estimating the Relationship between Multivariable Standing Ankle Impedance and Lower Extremity Muscle Activation.
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics,
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