Quantifying Performance of Bipedal Standing with Multi-channel EMG
This work addresses the need for automated assessment in spinal rehabilitation therapies, offering a tool for real-time feedback in combined stimulation and robotic systems, though it is incremental as it builds on existing EMG analysis methods.
The study tackled the problem of quantifying bipedal standing quality in spinal cord injury patients under spinal stimulation by correlating multi-channel EMG features with expert evaluations, showing that EMG can provide accurate and fast estimation and that channel count can be reduced while maintaining high accuracy.
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings.