TY - JOUR
T1 - Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy
AU - Conner, Benjamin C.
AU - Spomer, Alyssa M.
AU - Steele, Katherine M.
AU - Lerner, Zachary F.
N1 - Publisher Copyright:
© 2022 RESNA.
PY - 2022
Y1 - 2022
N2 - A current limitation in the development of robotic gait training interventions is understanding the factors that predict responses to treatment. The purpose of this study was to explore the application of an interpretable machine learning method, Bayesian Additive Regression Trees (BART), to identify factors influencing neuromuscular responses to a resistive ankle exoskeleton in individuals with cerebral palsy (CP). Eight individuals with CP (GMFCS levels I–III, ages 12–18 years) walked with a resistive ankle exoskeleton over seven visits while we measured soleus activation. A BART model was developed using a predictor set of kinematic, device, study, and participant metrics that were hypothesized to influence soleus activation. The model (R 2 = 0.94) found that kinematics had the largest influence on soleus activation, but the magnitude of exoskeleton resistance, amount of gait training practice with the device, and participant-level parameters also had substantial effects. To optimize neuromuscular engagement during exoskeleton training in individuals with CP, our analysis highlights the importance of monitoring the user’s kinematic response, in particular, peak stance phase hip flexion and ankle dorsiflexion. We demonstrate the utility of machine learning techniques for enhancing our understanding of robotic gait training outcomes, seeking to improve the efficacy of future interventions.
AB - A current limitation in the development of robotic gait training interventions is understanding the factors that predict responses to treatment. The purpose of this study was to explore the application of an interpretable machine learning method, Bayesian Additive Regression Trees (BART), to identify factors influencing neuromuscular responses to a resistive ankle exoskeleton in individuals with cerebral palsy (CP). Eight individuals with CP (GMFCS levels I–III, ages 12–18 years) walked with a resistive ankle exoskeleton over seven visits while we measured soleus activation. A BART model was developed using a predictor set of kinematic, device, study, and participant metrics that were hypothesized to influence soleus activation. The model (R 2 = 0.94) found that kinematics had the largest influence on soleus activation, but the magnitude of exoskeleton resistance, amount of gait training practice with the device, and participant-level parameters also had substantial effects. To optimize neuromuscular engagement during exoskeleton training in individuals with CP, our analysis highlights the importance of monitoring the user’s kinematic response, in particular, peak stance phase hip flexion and ankle dorsiflexion. We demonstrate the utility of machine learning techniques for enhancing our understanding of robotic gait training outcomes, seeking to improve the efficacy of future interventions.
KW - Bayesian additive regression tree
KW - cerebral palsy
KW - device engagement
KW - exoskeleton
KW - neurorehabilitation
UR - http://www.scopus.com/inward/record.url?scp=85139446059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139446059&partnerID=8YFLogxK
U2 - 10.1080/10400435.2022.2121324
DO - 10.1080/10400435.2022.2121324
M3 - Article
AN - SCOPUS:85139446059
SN - 1040-0435
JO - Assistive Technology
JF - Assistive Technology
ER -