Sharifi Iman, Talebi Heidar Ali, Patel Rajni R, Tavakoli Mahdi
Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran.
Electrical & Computer Engineering Department, Western University, London, ON, Canada.
Front Robot AI. 2020 Nov 11;7:538347. doi: 10.3389/frobt.2020.538347. eCollection 2020.
In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a "hands-on" manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices ( and ) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve "Simultaneous Training and Therapy" in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.
本文提出了一种用于多边远程康复的新方案。在该方案中，有一名治疗师、一名患者和若干学员参与远程康复（TR）过程。这种策略有助于治疗师远程促进神经康复。这样，患者可以居家，从而更安全且成本更低。同时，医学教育中心的若干学员可以通过部分参与康复过程接受培训。学员以“亲身实践”的方式参与；因此，他们感觉自己是在直接为患者进行康复治疗。为实施这样的方案，基于多智能体系统（MAS）中的自智能，提出了一种将MAS理论的力量应用于多边遥操作的新颖理论方法。在先前的相关工作中，改变多边遥操作任务中的参与者数量需要重新设计控制器；而在本文中，利用MAS的分散控制和自智能，在所提出的结构中避免了重新设计控制器的需求。此外，在本研究中，考虑了操作员动态中的不确定性以及通信通道中的时变延迟。结果表明，所提出的结构有两个调谐矩阵（和），可用于多边遥操作的不同场景。通过选择合适的调谐矩阵，可以实现许多关于多边遥操作/远程康复过程的相关工作。在论文的最后部分，介绍了几种在TR中实现“同步训练与治疗”的场景，并在所提出的结构上进行了实现。结果证实了所提出框架的稳定性和性能。
