Ferrante S, Ambrosini E, Ferrigno G, Pedrocchi A
Neuroengineering and medical robotics Laboratory, Bioengineering Department, Politecnico di Milano, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1888-91. doi: 10.1109/EMBC.2012.6346321.
The European Project MUltimodal Neuroprosthesis for Daily Upper limb Support (MUNDUS) aims at the development of an assistive platform for recovering direct interaction capability during daily life activities based on arm reaching and hand functions. Within this project the present study is focused on the design of a biomimetic controller able to modulate the neuromuscular electrical stimulation needed to perform reaching movements supported by a commercial passive exoskeleton for weight relief. Once defined the activities of daily life to be supported by the MUNDUS system, an experimental campaign on healthy subjects was carried out to identify the repeatable kinematics and muscular solution adopted during the target movements. The kinematics resulted to be highly stereotyped, a root mean squared error lower than 5° was found between all the trajectories obtained by healthy subjects in the same movement. A principal component analysis was performed on the EMG signals: less than 5 components explained more than the 85% of the signal variance. This result suggested that the muscular strategy adopted by healthy subjects was stereotyped and can be replicated by a biomimetic NMES controller. The controller was based on a time-delay artificial neural network which mapped the dynamic and non-linear relationship between kinematics and EMG activations to determine the stimulation timing. The stimulation levels reproduced the same scaling factors found between muscles in the stereotyped strategy. The controller was tested on 2 healthy subjects and though it was a feedforward controller, it showed good accuracy in reaching the desired target positions. The integration of a feedback controller is foreseen to ensure the complete accomplishment of the task and to compensate for unpredictable conditions such as muscular fatigue.
欧洲“用于日常上肢支撑的多模态神经假体”（MUNDUS）项目旨在开发一个辅助平台，以恢复基于手臂伸展和手部功能的日常生活活动中的直接交互能力。在该项目中，本研究专注于设计一种仿生控制器，该控制器能够调节神经肌肉电刺激，以执行由商业被动外骨骼提供减重支持的伸展运动。一旦确定了MUNDUS系统要支持的日常生活活动，便对健康受试者开展了一项实验活动，以确定目标运动过程中采用的可重复运动学和肌肉解决方案。结果表明运动学具有高度的刻板性，在相同运动中健康受试者获得的所有轨迹之间发现均方根误差低于5°。对肌电图信号进行了主成分分析：少于5个成分解释了超过85%的信号方差。这一结果表明，健康受试者采用的肌肉策略是刻板的，并且可以由仿生神经肌肉电刺激控制器复制。该控制器基于时延人工神经网络，该网络映射运动学与肌电图激活之间的动态和非线性关系，以确定刺激时机。刺激水平再现了刻板策略中肌肉之间相同的比例因子。该控制器在2名健康受试者身上进行了测试，尽管它是一个前馈控制器，但在到达期望目标位置方面显示出良好的准确性。预计将集成一个反馈控制器，以确保任务的完全完成，并补偿诸如肌肉疲劳等不可预测的情况。
