Amina El-Zatmah presents at the CNT 2023 Summer Undergraduate Research Symposium

Amina is wearing the Biomotum Spark exoskeleton while standing in front of her poster at her CNT presentation.This summer, the Steele Lab hosted undergraduate researcher, Amina El-Zatmah, from Santa Monica College. She finished up her 10-week summer Research Experience for Undergraduate (REU) by presenting at the 2023 Summer Undergraduate Research Symposium with the Center for Neurotechnology (CNT).

Amina gave a podium and poster presentation titled “Take A Step: The Effects of Transcutaneous Spinal Cord Stimulation and Exoskeleton Use on Step Length for Children with Cerebral Palsy“.

Amina was supported through mentorship from Charlotte Caskey, Siddhi Shrivastav, Chet Moritz, and Kat Steele.

Way to go, Amina!

 

ASB 2023 Recap

Charlotte is wearing a striped dress and black blazer standing in front of her poster at ASB.Four members of our lab – Kat, Elijah, Charlotte, & Mackenzie – attended ASB 2023 on August 8-11 in Knoxville, TN.

Elijah Kuska gave a podium presentation on “The effects of weakness, contracture, and altered control on walking energetics during crouch gait.”

Charlotte Caskey gave a poster presentation on “The effect of increased sensory feedback from neuromodulation and exoskeleton use on ankle co-contraction in children with cerebral palsy.”

Kat Steele co-hosted a workshop on “Writing a Successful NIH R01 Proposal.”

ASB 2024 will be hosted August 5-8, in Madison, WI.

 

 

Elijah is wearing a striped polo shirt and giving a presentation in front of a group of people at ASB.

MR Ebers, MC Rosenberg, JN Kutz, KM Steele (2023) “A machine learning approach to quantify individual gait responses to ankle exoskeletons”

Journal Article in Journal of Biomechanics:

Physiological and biomechanical responses to mechanical assistance from wearable technology are highly variable, especially for clinical populations; tools to predict how users respond to different types of exoskeleton assistance may optimize the prescription process and uncover underlying mechanisms driving locomotor changes in the context of personalized wearable/assistive technology.

Aim: The purpose of this study was to determine if a discrepancy modeling framework could quantify individual-specific gait responses to ankle exoskeletons.

Method: We employ a machine learning technique — neural network based discrepancy modeling — on gait data from 12 non-disabled adults to capture within-participant differences in walking dynamics without vs. with a bilateral passive elastic ankle exoskeletons applying 5 N-m/deg of torque. We fit three models: Nominal gait (no exo), Exo, and Discrepancy. Then, post-fitting, we extend the Nominal by the Discrepancy Model (Augmented). We hypothesize that if Augmented (Nom+Discrep) can capture similar amount of variability as the Exo model, then it can be inferred that the discrepancy model accurately captures how a user will respond to an exoskeleton — without direct information about that user’s physiology or motor coordination.

Results:While joint kinematics during Exo gait were well predicted using the Nominal model (median 𝑅2 = 0.863 − 0.939), the Augmented model significantly increased variance accounted for (𝑝 < 0.042, median 𝑅2 = 0.928 − 0.963). For EMG, the Augmented model (median 𝑅2 = 0.665 −
0.788) accounted for significantly more variance than the Nominal model (median 𝑅2 = 0.516 − 0.664). Minimal kinematic variance was left unexplained by the Exo model (median 𝑅2 = 0.954 − 0.978), but only accounted for 72.4%–81.5% of the median variance in EMG during Exo gait across all individuals.

Interpretation:Discrepancy modeling successfully quantified individuals’ exoskeleton responses without requiring knowledge about physiological structure or motor control. However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait.

RESNA 2023 Conference: Mia Hoffman receives Student Scientific Paper Award

Nicole wearing a black dress and Mia wearing a floral dress standing in front of a large sign at the RESNA conference.Two lab members, Nicole Zaino and Mia Hoffman attended the annual Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) Conference on July 24-26 in New Orleans, LA.

Big congratulations to Mia Hoffman for being selected as an awardee in the Student Scientific Paper Competition (SSPC).

Mia gave a podium presentation on “Exploring the World on Wheels: A Geospatial Comparison of Two Pediatric Mobility Devices

Nicole was also selected to give an interactive poster presentation on “Quantifying Toddler Exploration in Seated and Standing Postures with Powered Mobility“. She also completed her time as the student board member for RESNA.

Way to go, Mia and Nicole!

AM Spomer, RZ Yan, MH Schwartz, KM Steele (2023) “Motor control complexity can be dynamically simplified during gait pattern exploration using motor control-based biofeedback”

Journal Article in Journal of Neurophysiology

Understanding how the central nervous system coordinates diverse motor outputs has been a topic of extensive investigation. Although it is generally accepted that a small set of synergies underlies many common activities, such as walking, whether synergies are equally robust across a broader array of gait patterns or can be flexibly modified remains unclear.

Schematic of the custom biofeedback system. A) Motor control biofeedback used to encourage pattern exploration. B) Individuals significantly modified motor control complexity using biofeedback. C) Distal gait mechanics were associated with changes in control complexity.Aim: The aim of this study was to characterize the robustness of synergies to changing biomechanical constraints during walking. Specifically, we evaluated the extent to which nondisabled individuals could modulate both synergy structure and complexity while using motor control biofeedback to drive broad gait pattern exploration.

Methods: We evaluated the extent to which synergies changed as nondisabled adults (n = 14) explored gait patterns using custom biofeedback. Secondarily, we used Bayesian additive regression trees to identify factors that were associated with synergy modulation.

Results: Participants explored 41.1 ± 8.0 gait patterns using biofeedback, during which synergy recruitment changed depending on the type and magnitude of gait pattern modification. Specifically, a consistent set of synergies was recruited to accommodate small deviations from baseline, but additional synergies emerged for larger gait changes. Synergy complexity was similarly modulated; complexity decreased for 82.6% of the attempted gait patterns, but distal gait mechanics were strongly associated with these changes. In particular, greater ankle dorsiflexion moments and knee flexion through stance, as well as greater knee extension moments at initial contact, corresponded to a reduction in synergy complexity.

Interpretation: Taken together, these results suggest that the central nervous system preferentially adopts a low-dimensional, largely invariant control strategy but can modify that strategy to produce diverse gait patterns. Beyond improving understanding of how synergies are recruited during gait, study outcomes may also help identify parameters that can be targeted with interventions to alter synergies and improve motor control after neurological injury.

New & Noteworthy: We used a motor control-based biofeedback system and machine learning to characterize the extent to which nondisabled adults can modulate synergies during gait pattern exploration. Results revealed that a small library of synergies underlies an array of gait patterns but that recruitment from this library changes as a function of the imposed biomechanical constraints. Our findings enhance understanding of the neural control of gait and may inform biofeedback strategies to improve synergy recruitment after neurological injury.