{'fraction_non_alphanumeric': 0.022207707380796866, 'fraction_numerical': 0.001959503592423253, 'mean_word_length': 5.410041841004184, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Velocity Field based Active-Assistive Control for Upper Limb Rehabilitation Exoskeleton Robot

There are limitations of conventional active-assistive control for upper limb rehabilitation exoskeleton robot, such as 1). prior time-dependent trajectories are generally required, 2). task-based rehabilitation exercise involving multi-joint motion is hard to implement, and 3). assistive mechanism normally is so inflexible that the resulting exercise performed by the subjects becomes inefficient. In this paper, we propose a novel velocity field based active-assistive control system to address these issues. First, we design a Kalman filter based interactive torque observer to obtain subjects’ active intention of motion. Next, a joint-position-dependent velocity field which can be automatically generated via the task motion pattern is proposed to provide the time-independent assistance to the subjects. We further propose a novel integration method that combines the active and assistive motions based on the performance and the involvement of subjects to guide them to perform the task more voluntarily and precisely. The experiment results show that both the execution time and the subjects’ torque exertion are reduced while performing both given single joint tasks and task-oriented multi-joint tasks as compared with the related work in the literature. To sum up, the proposed system not only can efficiently retain subjects’ active intention but also can assist them to accomplish the rehabilitation task more precisely.