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

Synergy structure and activations had minimal changes with increasing exoskeleton torque.

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.Synergy structure and activations had minimal changes with increasing exoskeleton torque.

Heather Feldner’s manuscript accepted to Frontiers Robotics and AI

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

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

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

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.

jessicaz

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

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.

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

J Wu, BR Shuman, BW Brunton, KM Steele, JD Olson, RPN Rao (2016) “Multistep model for predicting upper-limb 3D isometric force application from pre-movement electrocorticographic features.” IEEE Engineering Medicine & Biology

Example of ECoG recording during upper-extremity force production.

Peer-reviewed paper at IEEE Engineering in Medicine & Biology Annual Conference:

Can we estimate upper-extremity force production from electrocorticographic recordings?

Example of ECoG recording during upper-extremity force production.Abstract: Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning.