We present two types of subject-specific assist-as-needed controllers for the index finger module of a hand exoskeleton designed for rehabilitation after a neuromuscular impairment such as stroke. Learned force-field control is a novel control technique in which a neural-network-based model of the required torques is learned for a specific subject and then used to build a force-field to assist the subject’s finger joint motion. Adaptive assist-as-needed control, on the other hand, estimates the coupled finger-exoskeleton system torque requirement of a subject using radial basis function (RBF) and on-the-fly adapts the RBF magnitudes to provide a feed-forward assistance for improved trajectory tracking. Experiments on the index finger exoskeleton prototype with a healthy subject showed that while the force-field control is non-adaptive and there is less control on the speed of execution of the task, it is safer as it does not apply increased torques if the finger motion is restricted. On the other hand, adaptive assist-as-needed controller adapts to the changing needs of the coupled finger-exoskeleton system and helps in performing the task with a consistent speed, however, applies increased torques in case of restricted motion resulting in potential user discomfort.

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