Quantifying Performance of Bipedal Standing with Multi-channel EMG

November 21, 2017 ยท Declared Dead ยท ๐Ÿ› IEEE/RJS International Conference on Intelligent RObots and Systems

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Authors Yanan Sui, Kun ho Kim, Joel W. Burdick arXiv ID 1711.07894 Category stat.ML: Machine Learning (Stat) Cross-listed cs.AI, q-bio.NC Citations 8 Venue IEEE/RJS International Conference on Intelligent RObots and Systems Last Checked 4 months ago
Abstract
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.
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