Congratulations to Dr. Megan Ebers on earning her Doctorate in Mechanical Engineering! Dr. Eber’s PhD thesis dissertation was titled Machine Learning for Dynamical Models of Human Movement. Congratulations and best of luck as you move forward as a Postdoc in the AMATH department at UW!
Author: Yusuke Maruo
Introducing Dr. Elijah Kuska
Congratulations to Dr. Elijah Kuska on earning his Doctorate in Mechanical Engineering! Dr. Kuska’s PhD thesis dissertation was titled In Silico Techniques to Improve Understanding of Gait in Cerebral Palsy. Congratulations and best of luck as you move forward as an assistant teaching professor at the Colorado School of Mines!
MR Ebers, MC Rosenberg, JN Kutz, KM Steele (2023) “A machine learning approach to quantify individual gait responses to ankle exoskeletons”
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.
MH Schwartz, KM Steele, AJ Ries, AG Georgiadis, BA MacWilliams (2022) “A model for understanding the causes and consequences of walking impairments”
Journal Article in PLOS ONE:
Causal inference is inherently ambiguous since we cannot observe multiple realizations of the same person with different characteristics. Causal models must be evaluated through indirect means and reasoning.
Aim: The main objectives in conducting this study were to (1) propose a comprehensive model for quantifying the causes and consequences of walking impairments and (2) demonstrate the potential utility of the model for supporting clinical care and addressing basic scientific questions related to walking.
Method: This paper introduced a model consisting of 10 nodes and 23 primary causal paths and demonstrated the model’s utility using a large sample of gait data.
Results: The model was plausible, captured some well-known cause-effect relationships, provided new insights into others, and generated novel hypotheses requiring further testing through simulation or experiment.
Interpretation: This model is a proposal that is meant to be critically evaluated, validated or refuted, altered, and improved over time. Such improvements might include the introduction of new nodes, variables, and paths.
BC Conner, AM Spomer, KM Steele, ZF Lerner (2022) “Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy”
Journal Article in RESNA:
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.