Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy.
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 identif...
| Publicado en: | Assistive Technology Vol. 35; no. 6; pp. 463 - 471 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173272640&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173272640 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10400435 YVP jtl: Assistive Technology issn: 10400435 maglogo: Y pubinfo: dt: 2023 vid: 35 iid: 6 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 173272640 159474218 173272640 173272640 10.1080/10400435.2022.2121324 173272640 ppf: 463 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Factors influencing neuromuscular responses to gait training with a robotic ankle exoskeleton in cerebral palsy. aug: au: Conner, Benjamin C. Spomer, Alyssa M. Steele, Katherine M. Lerner, Zachary F. affil: College of Medicine – Phoenix, University of Arizona, Phoenix, Arizona, USA sug: subj: Neuromuscular Control Evaluation Gait Training Exoskeleton Devices Utilization Ankle Joint Physiology Cerebral Palsy Rehabilitation Walking Physiology Kinematics Human Child Adolescence Male Female Walking Speed Dorsiflexion Plantarflexion Flexion Extension Scales Electromyography Biomechanics Treadmills Regression Data Analysis Software Models, Statistical Descriptive Statistics Hip Joint Physiology Knee Joint Physiology Muscle Spasticity Soleus Muscles Physiology Arizona Clinical Assessment Tools Funding Source Machine Learning Child: 6-12 years Adolescent: 13-18 years Male Female 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 (R2 = 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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