Exoskeleton Archives - Steele Lab - University of Washington

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

Katie Landwehr News, Presentation September 1, 2023September 12, 2023Cerebral Palsy, Exoskeleton, Gait

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

Katie Landwehr News, Presentation August 25, 2023September 13, 2023Cerebral Palsy, Control, Crouch Gait, Exoskeleton, Motor Control, Simulation

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.

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

Yusuke Maruo JournalArticle, Publications February 9, 2023February 9, 2023Exoskeleton, Gait, Modeling

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.

BC Conner, AM Spomer, KM Steele, ZF Lerner (2022) “Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy”

Yusuke Maruo JournalArticle, Publications February 7, 2023August 9, 2023AFO, Cerebral Palsy, Exoskeleton

Journal Article in Assistive Technology:

Our findings underscored the importance of monitoring how users change their gait kinematics when walking with the resistive device, with a specific emphasis on stance-phase lower limb extension. We also highlight the necessity of considering an individual’s functional status and amount of practice with the device, as well as more obvious factors, like device parameters. BART can be used early in the development of robotic gait training interventions to better understand complex and multifactorial user-device interactions.

Aim: Although ankle exoskeletons offer a promising means of augmenting gait training and enhancing independent mobility among individuals with neuromuscular disorders, response to existing paradigms is highly heterogeneous. In this study we aimed to identify factors which may affect how individuals with cerebral palsy (CP) interact with a resistive ankle exoskeleton during multi-day training to inform future device design and individualized tuning.

Method:We evaluated the gait mechanics (kinematics and muscle activity) of eight individuals with CP as they walked with bilateral ankle exoskeletons – designed to promote increased plantar flexor recruitment – during a seven-day training paradigm. These data along with pertinent device and participant parameters were input into a Bayesian Additive Regression Trees (BART) machine learning model to identify factors which were most associated with increased plantar flexor recruitment.

Results: Four themes emerged: 1) AFO provision is a confusing and lengthy process, 2) participants want more information during AFO provision, 3) AFOs are uncomfortable and difficult to use, and 4) AFOs can benefit mobility and independence. Caregivers and individuals with CP recommended ideas such as 3D printing orthoses and education for caregivers on design choices to improve AFO design and provision.

Interpretation: Individuals with CP and their caregivers found the AFO provision process frustrating but highlight that AFOs support mobility and participation. Further opportunities exist to support function and participation of people with CP by streamlining AFO provision processes, creating educational materials, and improving AFO design for comfort and ease of use.

MC Rosenberg, BS Banjanin, SA Burden, KM Steele (2020) “Predicting walking response to ankle exoskeleton using data driven models”

Elijah JournalArticle, News October 20, 2020October 12, 2021Exoskeleton, Floquent theory, Gait, generalizable models, Modeling, Orthoses, Walking

Journal Article in The Royal Society:

This work highlights the potential of data-driven models grounded in dynamical systems theory to predict complex individualized responses to ankle exoskeletons., without requiring explicit knowledge of the individual’s physiology or motor control

Aim: Evaluate the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics.

Method: Data from 12 unimpaired adults walking with bilateral passive ankle exoskeletons were captured. PV, linear PV (LPV), and nonlinear PV (NPV) models leveraged Floquet theory to kinematics and muscle activity in response to three exoskeleton torque conditions.

Results: The LPV model’s predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49–70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses.

Interpretation: This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

← Previous