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.

机器人设备已经被开发出来以提高康复治疗的效率并降低成本。为了真实的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。现有的方法来估计相互作用力-扭矩是有限的。目前用于估计交互式人体力-扭矩的测量技术是肌电图（EMG）和电阻抗肌电图（EIM）。EMG和EIM的融合将受益于这两种模式，因此，应该提供一个新的定量评估工具的肌肉活动。本计画的目标是发展一个强健的演算法，借由结合肌电与肌肉感应法，来正确估计肌肉的力矩与力量。最近，Schultheis等人提出了将EMG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。该项目旨在将俄罗斯和德国团队的专业知识和经验联合收割机结合起来。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，研究最佳电极配置和开发用于验证的测量原型，以及EMG和EIM的算法融合以进行精确的力/扭矩估计。电有限元建模和仿真将被认为是研究EIM信号形成的数学仿真工具。一个新的原型的测量系统将开发相对于最佳的电极配置和测量设置来自实验和有限元模拟数据。EMG和EIM的融合算法将被开发考虑基于模型或基于信号的方法。将在运动实验室中对健康志愿者进行整体测试和确认，并结合机械测试台。