Investigating Biomechanical Adaptations to Personalized Wearable Assistive Devices

This thesis investigates the importance of considering individual biomechanical and neuromuscular responses when designing personalized robotic assistance and training. A KINARM study revealed substantial variability in upper extremity muscle coordination during reaching, suggesting current standardized controls may be insufficient. Examining adaptation and factors influencing motor responses provided insights into customizing assistance via biofeedback. Likewise, a hip exosuit study highlighted the benefits of structured training for users to optimize device support. Analyzing quick biomechanical adjustments made when integrating assistance revealed critical timeframes for refining learned patterns. Significant variances in motor control and learning strategies emphasize personalizing devices to account for population heterogeneity. Identifying biomarkers of adaptation allows fine-tuning assistance based on evolving user capabilities and integrating biofeedback for a more responsive, natural experience. These results drive transforming rigid technologies into versatile interventions that encourage natural movement and facilitate diverse therapeutic goals, enhancing the practicality of robotic devices for rehabilitation.