Quantifying Performance of Bipedal Standing with Multi-channel EMG
November 21, 2017 ยท Declared Dead ยท ๐ IEEE/RJS International Conference on Intelligent RObots and Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Machine Learning (Stat)
๐ฎ
๐ฎ
The Ethereal
๐ฎ
๐ฎ
The Ethereal
Layer Normalization
๐ฎ
๐ฎ
The Ethereal
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
R.I.P.
๐ป
Ghosted
Variational Inference with Normalizing Flows
๐
๐
The Cartographer
Towards A Rigorous Science of Interpretable Machine Learning
R.I.P.
๐ป
Ghosted
Optimization Methods for Large-Scale Machine Learning
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted