Multimodal engagement estimation in paediatric robot-assisted gait training: integrating physiological sensing and biomechanical interaction

Background: Patients' engagement plays a crucial role in the effectiveness of robot-assisted gait training (RAGT), particularly in paediatric neurorehabilitation, where motivation and active participation strongly influence functional outcomes. Despite its clinical relevance, engagement assessment is still largely based on subjective observation, limiting objectivity and scalability. Objective modelling of engagement in children with cerebral palsy remains underexplored, especially when relying on physiological signals acquired during therapy, due to logistical, ethical, and technological challenges that constrain multimodal data collection and cohort sizes. This study proposes a novel multimodal framework to automatically estimate engagement in a paediatric cerebral palsy population undergoing RAGT, capturing clinically relevant variability across patients. Methods: A cohort of 20 paediatric patients with cerebral palsy undergoing RAGT with the Hocoma Lokomat® system was monitored across multiple therapy sessions. Heart rate variability (HRV), facial infrared thermography (IRT), and exoskeleton-derived biomechanical features were acquired to capture complementary physiological and behavioural responses related to engagement. Signals were segmented into time windows and processed to extract statistical, spectral, and complexity features. Engagement was annotated by an expert clinician using a three-class scale (not engaged, neutral, engaged). A supervised multiclass machine-learning framework was then developed to estimate engagement from multimodal data, evaluating several ensemble-based classifiers under strict training-testing separation with repeated stratified splits and class-weighted learning to tackle class imbalance. Results: The combination of multimodal features with the Extra Trees classifier achieved the best performance, with a macro-F1 score of 0.658 ± 0.027. Class-wise analysis showed higher performance for extreme engagement levels (not engaged and engaged), with most misclassifications occurring between adjacent classes, particularly involving the neutral state. Feature selection and explainability analyses identified exoskeleton-derived torque features as the most influential predictors, with thermal and HRV features providing complementary autonomic and emotional information. Conclusions: This study presents a novel multimodal machine learning approach for automatic engagement estimation during RAGT in children with cerebral palsy. Beyond classification performance, it demonstrates the feasibility of objective engagement modelling in a complex clinical population by integrating physiological and robotic interaction data. The results highlight the potential of multimodal sensing to support adaptive rehabilitation systems for continuous monitoring and personalized human-robot interaction.