OBJECTIVE: Biomechanical models can inform design and optimization of prosthetic devices by connecting empirically derived biomechanical data to device design parameters. A new method is presented to characterize the function of prosthetic stance control under mobility conditions associated with activities of daily living. The method is based on a model of the gait modes corresponding to finite stance control states. METHODS: Empirical data from amputee and simulated gait were acquired using a custom-built wearable instrument and input into the model. RESULTS: The modeling approach was shown to be robust, responsive, and capable of accurate characterization of controller function under diverse locomotor and prosthetic setup conditions. CONCLUSION: Future work is focused on the development of a fully self-contained wearable system, to facilitate collection of large datasets across a variety of user demographics, controller designs, and activities of daily living. SIGNIFICANCE: The method offers predictive capability, which can assist in the virtual testing of new designs or modifications.
OBJECTIVE: Biomechanical models can inform design and optimization of prosthetic devices by connecting empirically derived biomechanical data to device design parameters. A new method is presented to characterize the function of prosthetic stance control under mobility conditions associated with activities of daily living. The method is based on a model of the gait modes corresponding to finite stance control states. METHODS: Empirical data from amputee and simulated gait were acquired using a custom-built wearable instrument and input into the model. RESULTS: The modeling approach was shown to be robust, responsive, and capable of accurate characterization of controller function under diverse locomotor and prosthetic setup conditions. CONCLUSION: Future work is focused on the development of a fully self-contained wearable system, to facilitate collection of large datasets across a variety of user demographics, controller designs, and activities of daily living. SIGNIFICANCE: The method offers predictive capability, which can assist in the virtual testing of new designs or modifications.