PD64-03 DEMONSTRATION OF LEVATOR ANI EMG ACTIVITY BELOW THE LEVEL OF INJURY IN COMPLETE SPINAL CORD INJURY (SCI) USING OVER GROUND ROBOTIC EXOSKELETON WALKING

2017 ◽  
Vol 197 (4S) ◽  
Author(s):  
Lynn Stothers ◽  
Chisholm Amanda ◽  
Raid Alamro ◽  
A Williams ◽  
Tania Lam
2017 ◽  
Vol 41 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Edward D. Lemaire ◽  
Andrew J. Smith ◽  
Andrew Herbert-Copley ◽  
Vidya Sreenivasan

2017 ◽  
Vol 31 (6) ◽  
pp. 583-591 ◽  
Author(s):  
Elizabeth Heald ◽  
Ronald Hart ◽  
Kevin Kilgore ◽  
P. Hunter Peckham

Background. Previous studies have demonstrated the presence of intact axons across a spinal cord lesion, even in those clinically diagnosed with complete spinal cord injury (SCI). These axons may allow volitional motor signals to be transmitted through the injury, even in the absence of visible muscle contraction. Objective. To demonstrate the presence of volitional electromyographic (EMG) activity below the lesion in motor complete SCI and to characterize this activity to determine its value for potential use as a neuroprosthetic command source. Methods. Twenty-four subjects with complete (AIS A or B), chronic, cervical SCI were tested for the presence of volitional below-injury EMG activity. Surface electrodes recorded from 8 to 12 locations of each lower limb, while participants were asked to attempt specific movements of the lower extremity in response to visual and audio cues. EMG trials were ranked through visual inspection, and were scored using an amplitude threshold algorithm to identify channels of interest with volitional motor unit activity. Results. Significant below-injury muscle activity was identified through visual inspection in 16 of 24 participants, and visual inspection rankings were well correlated to the algorithm scoring. Conclusions. The surface EMG protocol utilized here is relatively simple and noninvasive, ideal for a clinical screening tool. The majority of subjects tested were able to produce a volitional EMG signal below their injury level, and the algorithm developed allows automatic identification of signals of interest. The presence of this volitional activity in the lower extremity could provide an innovative new command signal source for implanted neuroprostheses or other assistive technology.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Samineh Mesbah ◽  
Federica Gonnelli ◽  
Claudia A. Angeli ◽  
Ayman El-baz ◽  
Susan J. Harkema ◽  
...  

Abstract The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation.


Sign in / Sign up

Export Citation Format

Share Document