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

• Applicant: Professor Dr.-Ing. Steffen Leonhardt
• Subject Area: Biomedical Systems Technology
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 429544861

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.

• DFG Programme: Research Grants
• International Connection: Russia
• Partner Organisation: Russian Foundation for Basic Research
• Cooperation Partner: Professor Dr. Sergey Shchukin