This paper presents a grasp prediction algorithm designed to govern the motion of an exoskeletal glove in rehabilitative and assistive applications. Recent research into the dynamics of hand motion has shown that the complex motion of the finger joints can be represented as a smaller set of coordinated motions or latent variables. This fact forms the basis of the proposed algorithm capable of successful prediction even with noisy data. From relatively small motion (minute user hand movements) as the input, the developed algorithm can predict intended grasp configurations. The 16 finger joint angles, with random noise, are mapped onto a set of six latent variables for which the estimated noise and future configuration are simultaneously determined using a linear regression. The algorithm was tested in simulation on published motion data from 30 healthy subjects performing a set of common grasps on multiple objects. The algorithm was able to determine the target state with an accuracy of approximately 90% for each subject, despite the nonlinear motion and non-uniform trajectory variations. We propose that the predicted grasp is an adequate target for an exoskeletal glove to provide initial gross movement for the user, then iteratively converge to the desired grasp with only limited additional user input.
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