González-Mendoza Arturo, Quiñones-Urióstegui Ivett, Salazar-Cruz Sergio, Perez-Sanpablo Alberto-Isaac, López-Gutiérrez Ricardo, Lozano Rogelio
LAFMIA UMI, Center for Research and Advanced, Studies of National Polytechnic Institute, Av. Instituto Politécnico Nacional No. 2508, 07360 Mexico City, Mexico.
Motion Analysis Lab, National Institute of Rehabilitation Luis Guillermo Ibarra Ibarra, Calz. México Xochimilco No. 289, 14389 Mexico City, Mexico.
J Bionic Eng. 2022;19(5):1374-1391. doi: 10.1007/s42235-022-00214-z. Epub 2022 Jun 21.
This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human-robot interaction.
本文介绍了一种上肢外骨骼,它能够实现认知交互(通过肌电信号)和物理用户交互(通过称重传感器),用于被动和主动运动,从而在神经损伤患者的康复过程中激活神经可塑性。为了使外骨骼易于被神经损伤患者接受,我们采用ISO9241-210:2010作为方法设计流程。作为设计流程的第一步,从先前的可用性测试和文献中收集设计要求。然后,作为第二步,提出一种技术解决方案,作为第三步,通过性能和用户测试对系统进行评估。作为技术解决方案的一部分,并为了让患者在康复过程中参与进来,我们提出了一种混合导纳控制,其输入为称重传感器信号或肌电信号。该混合导纳控制适用于主动治疗运动,易于实现,且工作时无需肌肉骨骼建模。此外,还评估了肌电信号分类模型和特征,以确定认知人机交互的最佳设置。