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!
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.
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!
Journal Article in bioRxiv:
Discrepancy modeling is a unique and innovative tool that complements current biomechanical modeling approaches and may accelerate the discovery of individual-specific mechanisms driving responses to exoskeletons, other assistive devices, and clinical interventions.
Aim: This study aims to leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in non-disabled adults. It hypothesized that (i) the Nominal model would predict Exo kinematics and EMG less accurately than for the Nominal condition, and (ii) the Augmented (Nominal+Discrepancy) model would capture greater variance in Exo kinematics and EMG than the Nominal model.
Method: This study analyzed gait data for 12 non-disabled adults during treadmill walking in bilateral passive ankle exoskeletons at a self-selected speed, results of which were used in participant-specific continuous-time neural network with discrepancy models to predict gait responses.
Results: 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.
Interpretation: These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.