Publications Archives - Page 18 of 33 - Neuromechanics & Mobility Lab

BR Shuman, MH Schwartz, KM Steele (2017) “Electromyography Data Processing Impacts Muscle Synergies during Gait for Unimpaired Children and Children with Cerebral Palsy.” Frontiers in Computational Neuroscience

Dr. Steele JournalArticle, MotorControl, Projects, Publications May 19, 2017October 12, 2021Cerebral Palsy, EMG, Gillette, Synergy, UWIN, Walk-DMC

Journal article in Frontiers in Computational Neuroscience:

Filtering parameters impact the results from muscle synergy analyses.

Abstract: Muscle synergies calculated from electromyography (EMG) data identify weighted groups of muscles activated together during functional tasks. Research has shown that fewer synergies are required to describe EMG data of individuals with neurologic impairments. When considering potential clinical applications of synergies, understanding how EMG data processing impacts results and clinical interpretation is important. The aim of this study was to evaluate how EMG signal processing impacts synergy outputs during gait. We evaluated the impacts of two common processing steps for synergy analyses: low pass (LP) filtering and unit variance scaling. We evaluated EMG data collected during barefoot walking from five muscles of 113 children with cerebral palsy (CP) and 73 typically-developing (TD) children. We applied LP filters to the EMG data with cutoff frequencies ranging from 4 to 40 Hz (reflecting the range reported in prior synergy research). We also evaluated the impact of normalizing EMG amplitude by unit variance. We found that the total variance accounted for (tVAF) by a given number of synergies was sensitive to LP filter choice and decreased in both TD and CP groups with increasing LP cutoff frequency (e.g., 9.3 percentage points change for one synergy between 4 and 40 Hz). This change in tVAF can alter the number of synergies selected for further analyses. Normalizing tVAF to a z-score (e.g., dynamic motor control index during walking, walk-DMC) reduced sensitivity to LP cutoff. Unit variance scaling caused comparatively small changes in tVAF. Synergy weights and activations were impacted less than tVAF by LP filter choice and unit variance normalization. These results demonstrate that EMG signal processing methods impact outputs of synergy analysis and z-score based measures can assist in reporting and comparing results across studies and clinical centers.

KM Steele, RW Jackson, BR Shuman, SH Collins (2017) “Muscle recruitment and coordination with an ankle exoskeleton.” Journal of Biomechanics

Dr. Steele JournalArticle, MotorControl, Orthoses May 12, 2017October 12, 2021AFO, Control, Synergy, Walk-DMC

Journal article in Journal of Biomechanics:

How do muscle activations and synergies change when an individual wears an ankle exoskeleton during gait?

Abstract: Exoskeletons have the potential to assist and augment human performance. Understanding how users adapt their movement and neuromuscular control in response to external assistance is important to inform the design of these devices. The aim of this research was to evaluate changes in muscle recruitment and coordination for ten unimpaired individuals walking with an ankle exoskeleton. We evaluated changes in the activity of individual muscles, cocontraction levels, and synergistic patterns of muscle coordination with increasing exoskeleton work and torque. Participants were able to selectively reduce activity of the ankle plantarflexors with increasing exoskeleton assistance. Increasing exoskeleton net work resulted in greater reductions in muscle activity than increasing exoskeleton torque. Patterns of muscle coordination were not restricted or constrained to synergistic patterns observed during unassisted walking. While three synergies could describe nearly 95% of the variance in electromyography data during unassisted walking, these same synergies could describe only 85–90% of the variance in muscle activity while walking with the exoskeleton. Synergies calculated with the exoskeleton demonstrated greater changes in synergy weights with increasing exoskeleton work versus greater changes in synergy activations with increasing exoskeleton torque. These results support the theory that unimpaired individuals do not exclusively use central pattern generators or other low-level building blocks to coordinate muscle activity, especially when learning a new task or adapting to external assistance, and demonstrate the potential for using exoskeletons to modulate muscle recruitment and coordination patterns for rehabilitation or performance.

Heather Feldner’s manuscript accepted to Frontiers Robotics and AI

Keshia News, Publications February 15, 2017October 12, 2021

Congratulations to Heather Feldner, a post-doct within our lab, for the upcoming technology report publication in the Biomedical Robotics section of Frontiers in Robotics and AI.

Title Toy-Based Technologies for Children with Disabilities Simultaneously Supporting Self-Directed Mobility, Participation and Function: A Tech Report

Authors Samuel W Logan, Heather Ann Feldner, Kathleen R Bogart, Brianna Goodwin, Samantha M Ross, Michele Ann Catena, Austin Allen Whitesell, Zachary Jordan Zefton, William D Smart, James Cole Galloway

Jessica Zistatsis to Present at UW Medicine – Inventor of the Year Event

Keshia Media, News, Presentation, Publications November 14, 2016October 12, 2021Orthoses

Tomorrow a group of esteemed faculty and students will present their work during the UW Inventor of the Year event at the Don James Center, Husky Stadium on November 15, 2016 from 5-7:30 p.m.

Samual Browd, Jonathan Posner, and Per Reinhall will be recognized for their collaborative work inventing and developing a football helmet designed to mitigate the forces thought to contribute to concussions. Jessica will be presenting a poster for her pediatric exoskeleton and competing in a lightning pitch competition.

B Soran, L Lowes, KM Steele (2016) “Evaluation of infants with spinal muscular atrophy using convolutional neural networks.” European Conference on Computer Vision

Dr. Steele JournalArticle, UbiquitousRehabilitation November 2, 2016October 12, 2021Kinect, MachineLearning, Pediatric, SMA

Experimental set-up with infant positioned below Kinect depth camera.

Peer-reviewed paper at European Conference on Computer Vision:

30-second videos from a depth camera can be used in the evaluation of infants with spinal muscular atrophy.

Abstract: Spinal Muscular Atrophy is the most common genetic cause of infant death. Due to its severity, there is a need for methods for automated estimation of disease progression. In this paper we propose a Convolutional-Neural-Network (CNN) model to estimate disease progression during infants’ natural behavior. With the proposed methodology, we were able to predict each child’s score on current behavior-based clinical exams with an average per-subject error of 6.96 out of 72 points (<10 % difference), using 30-second videos in leave-one-subject-out-cross-validation setting. When simple statistics were used over 30-second video-segments to estimate a score for longer videos, we obtained an average error of 5.95 ( $\sim$ 8 % error rate). By showing promising results on a small dataset (N $=$ 70, 2-minute samples, which were handled as 1487, 30-second video segments), our methodology demonstrates that it is possible to benefit from CNNs on small datasets by proper design and data handling choices.