Cavallaro Ettore E, Rosen Jacob, Perry Joel C, Burns Stephen
Department of Electrical Engineering, University of Washington, Seattle, WA 98185, USA.
IEEE Trans Biomed Eng. 2006 Nov;53(11):2387-96. doi: 10.1109/TBME.2006.880883.
Exoskeleton robots are promising assistive/rehabilitative devices that can help people with force deficits or allow the recovery of patients who have suffered from pathologies such as stroke. The key component that allows the user to control the exoskeleton is the human machine interface (HMI). Setting the HMI at the neuro-muscular level may lead to seamless integration and intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a model of the human muscle, the "myoprocessor," running in real-time and in parallel to the physiological muscle, that predicts joint torques as a function of the joint kinematics and neural activation levels. This paper presents the development of myoprocessors for the upper limb based on the Hill phenomenological muscle model. Genetic algorithms are used to optimize the internal parameters of the myoprocessors utilizing an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system.
外骨骼机器人是很有前景的辅助/康复设备，可帮助力量不足的人，或使中风等疾病患者康复。允许用户控制外骨骼的关键组件是人机接口（HMI）。将HMI设置在神经肌肉水平可能会导致外骨骼手臂作为人体的自然延伸实现无缝集成和直观控制。外骨骼HMI的核心是一个人体肌肉模型，即“肌处理器”，它与生理肌肉实时并行运行，根据关节运动学和神经激活水平预测关节扭矩。本文介绍了基于希尔现象学肌肉模型的上肢肌处理器的开发。利用遗传算法优化肌处理器的内部参数，使用一个实验数据库为模型提供输入并进行性能评估。结果表明模型的关节力矩预测与测量数据之间具有高度相关性。因此，肌处理器似乎是一个合适的模型，足够稳健可进一步集成到外骨骼控制系统中。
