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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation
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A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation

机译：上肢肢体康复的贪婪助手

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摘要

Previous studies on robotic rehabilitation have shown that subjects' active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the rehabilitation training, assist-as-needed (AAN) control strategies regulating the robotic assistance according to subjects' performance and conditions have been developed. Unfortunately, the heterogeneity of patients' motor function capability in task space is not taken into account during the implementation of these controllers. In this paper, a new scheme called greedy AAN (GAAN) controller is designed for the upper limb rehabilitation training of neurologically impaired subjects. The proposed GAAN control paradigm includes a baseline controller and a Gaussian RBF network that is utilized to model the functional capability of subjects and to provide corresponding a task challenge for them. In order to avoid subjects' slacking and encourage their active engagement, the weight vectors of RBF networks evaluating subjects' impairment level are updated based on a greedy strategy that makes the networks progressively learn the maximum forces over time provided by subjects. Simultaneously, a challenge level modification algorithm is employed to adjust the task challenge according to the task performance of subjects. Experiments on 12 subjects with neurological impairment are conducted to validate the performance and feasibility of the GAAN controller. The results show that the proposed GAAN controller has significant potential to promote the subjects' voluntary engagement during training exercises.
机译：先前有关机器人康复的研究表明，受试者在康复训练中的积极参与和努力可以促进治疗的效果。为了提高参与者在康复训练中的自愿性努力，已经开发了根据受试者的表现和状况调节机器人辅助的按需辅助（AAN）控制策略。不幸的是，在执行这些控制器的过程中，没有考虑患者运动功能在任务空间中的异质性。在本文中，设计了一种新的名为贪婪AAN（GAAN）控制器的方案，用于神经系统受损受试者的上肢康复训练。提出的GAAN控制范式包括基线控制器和高斯RBF网络，该网络用于对受试者的功能能力进行建模并为受试者提供相应的任务挑战。为了避免受试者的懈怠并鼓励他们积极参与，基于贪婪策略更新了评估受试者损伤水平的RBF网络的权重向量，该策略使网络逐渐了解受试者提供的最大力量。同时，采用挑战等级修改算法，根据被试的任务表现来调整任务挑战。为了验证GAAN控制器的性能和可行性，对12名神经系统受损的受试者进行了实验。结果表明，拟议的GAAN控制器具有极大的潜力，可以在训练过程中促进受试者的自愿参与。

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