Intramuscular EMG-driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

Objective: Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling. Methods: Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG. Results: The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients. Conclusion: We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients. Significance: Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation

Comparison of Intramuscular and Surface Electromyography Recordings towards the Control of Wearable Robots for Incomplete Spinal Cord Injury Rehabilitation

Spinal Cord Injury (SCI) affects thousands of people worldwide every year. SCI patients have disrupted muscle recruitment and are more predisposed to other complications. To recover or enhance lower limbs functions, conventional rehabilitation programs are typically used. More recently, conventional programs have been combined with robot-assisted training. Electromyography (EMG) activity is generally used to record the electrical activity of the muscles, which in turn can be used to control robotic assistive devices as orthoses, prostheses and exoskeletons. In this sense, surface EMG can be used as input to myoelectric control but presents some limitations such as myoelectric crosstalk, as well as the influence of motion artefacts, and electromagnetic noise. EMG can also be recorded using intramuscular detection systems, which allows the detection of electric potentials closer to the muscle fibres and the recording of EMG activity from deeper muscles. This paper evaluates the quality of intramuscular EMG recordings compared to surface EMG signals, as a preliminary step to control EMG-driven exoskeletons. Seven healthy subjects performed submaximal knee and ankle flexion/extension movements with and without the use of a lower limb exoskeleton. Intramuscular recordings presented early muscle activation detecting times, which is a very important feature in real-time control, and good signal-to-noise ratio values, showing the potential of these biosignals as reliable input measures to control exoskeletons for rehabilitation purposes