Model-Based Control of Individual Finger Movements for Prosthetic Hand Function

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IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society


Prosthetic devices for hand difference have advanced considerably in recent years, to the point where the mechanical dexterity of a state-of-the-art prosthetic hand approaches that of the natural hand. Control options for users, however, have not kept pace, meaning that the new devices are not used to their full potential. Promising developments in control technology reported in the literature have met with limited commercial and clinical success. We have previously described a biomechanical model of the hand that could be used for prosthesis control. The goal of this study was to evaluate the feasibility of this approach in terms of kinematic fidelity of model-predicted finger movement and the computational performance of the model. We show the performance of the model in replicating recorded hand and finger kinematics and find average correlations of 0.89 between modelled and recorded motions; we show that the computational performance of the simulations is fast enough to achieve real-time control with a robotic hand in the loop; and we describe the use of the model for controlling object gripping. Despite some limitations in accessing sufficient driving signals, the model performance shows promise as a controller for prosthetic hands when driven with recorded EMG signals. User-in-the-loop testing with amputees is necessary in future work to evaluate the suitability of available driving signals, and to examine translation of offline results to online performance.