Fusion of electromyogram and electrical impedance myography for force-torque estimation of human muscle contraction

肌电图和电阻抗肌电图融合用于人体肌肉收缩的力-扭矩估计

• 批准号: 429544861
• 负责人: Professor Dr.-Ing. Steffen Leonhardt
• 金额: --
• 依托单位: Lehrstuhl für Medizinische Informationstechnik (MedIT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/429544861?language=en
• 关键词: Fusion; electromyogram; electrical; impedance; myography

Robotic devices have been developed to increase the efficiency of rehabilitation therapy and reduce the cost. In order to assess and control the interactive force between the robotic devices and the users in real time, it is of importance to observe the user’s intention of motion. Existing methods to estimate the interactive force-torque are limited. Current measurement technologies for estimation of the interactive human force-torque are electromyography (EMG) and electrical impedance myography (EIM). A fusion of EMG and EIM will benefit from both modalities and hence, should provide a new quantitative assessment tool for muscle activity. It is the goal of this project to develop a robust algorithm for proper estimation of muscle torque and force by combining EMG with EIM. The general idea of combining EMG and bioimpedance measurements has recently been proposed by Schultheis et al. as an automated diagnostic instrument for treating swallowing disorders.The project is set up to combine expert knowledge and experiences from the Russian and the German team. Project objectives include the determination of main biophysical mechanisms of EIM signal formation during muscle contraction, the investigation on optimal electrode configuration and development of a measurement prototype for validation, and the algorithmic fusion of EMG and EIM for precise force/torque estimation. Electrical finite-element modeling and simulation will be considered as a mathematical simulative tool for the investigation of EIM signal formation. A new prototype of the measurement system will be developed with respect to the optimal electrode configuration and measurement setup deriving from experimental and FEM simulation data. An algorithm for the fusion of EMG and EIM will be developed considering model-based or signal-based approaches. The overall test and validation will be performed on healthy volunteers in a motion laboratory and in combination with a mechanical test bench.

机器人设备的发展是为了提高康复治疗的效率和降低成本。为了实时评估和控制机器人设备与用户之间的交互力，观察用户的运动意图是非常重要的。现有的相互作用力-扭矩估计方法存在局限性。目前用于估计人体相互作用力-扭矩的测量技术有肌电图（EMG）和肌电阻抗图（EIM）。肌电图和脑电图的融合将受益于这两种模式，因此，应该为肌肉活动提供一种新的定量评估工具。本项目的目标是通过肌电图和脑电图的结合，开发一种鲁棒的算法来正确估计肌肉的扭矩和力。最近，Schultheis等人提出了将肌电图和生物阻抗测量相结合作为治疗吞咽障碍的自动诊断仪器的总体思路。该项目结合了俄罗斯和德国团队的专家知识和经验。项目目标包括确定肌肉收缩过程中EIM信号形成的主要生物物理机制，研究最佳电极配置并开发用于验证的测量原型，以及将EMG和EIM融合的算法用于精确的力/扭矩估计。电气有限元建模和仿真将被视为研究电磁干扰信号形成的数学仿真工具。根据实验和有限元模拟数据，将开发一种新的测量系统原型，以优化电极配置和测量设置。将考虑基于模型或基于信号的方法，开发一种肌电信号和EIM融合的算法。整体测试和验证将在运动实验室中对健康志愿者进行，并结合机械测试台进行。