Characterizing and Predicting Submovements during Human Three-Dimensional Arm Reaches |
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Authors: | James Y. Liao Robert F. Kirsch |
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Affiliation: | 1. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America.; 2. Cleveland Functional Electrical Stimulation Center, Cleveland, Ohio, United States of America.; VU University Amsterdam, Netherlands, |
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Abstract: | We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs) to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration) of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces. |
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