Intelligent Robot-Aided, Task-Oriented Physiotherapy Method for Upper Limb Rehabilitation: Development and Usability...

Task-oriented rehabilitation supported by exoskeletons has the potential to increase therapy intensity, personalization, and accessibility. However, to achieve fully automatic treatment, robotized systems need to analyze therapy in a more complex way than only based on reference trajectories following.

This study aimed to investigate the effects of an intelligent, context-aware control algorithm for an upper limb rehabilitation exoskeleton on patients' musculoskeletal engagement, compared with constant-admittance robot-assisted therapy, and conventional physiotherapist-guided treatment.

A single-session experimental study was conducted with 34 adult participants performing 6 activities of daily living under 3 therapy modes: robot-assisted therapy with constant admittance, robot-assisted therapy with an intelligent assist-as-needed algorithm, and physiotherapist-guided therapy. Muscle activity was assessed using surface electromyography of 8 upper limb muscle groups, while joint kinematics were recorded using inertial measurement units. Metrics included electromyography power, muscle activation time, joint range of motion, and Burst Duration Similarity Indices. Statistical comparisons were performed using the t test and the Mann-Whitney U test depending on data normality.

Results indicate that the intelligent control strategy engages the musculoskeletal system at least as effectively as constant-admittance control across all exercises. At the same time, more motion control is given to the patient, which is consistent with the principles of neuroplastic motor learning. Compared with physiotherapist-guided therapy, robot-assisted treatment with intelligent control elicited significantly higher and more consistent muscular engagement. Intelligent assistance also modified joint-level motion patterns by reducing compensatory movements, particularly in shoulder-elbow coupling, while maintaining functional task execution. Muscle activation timing patterns during intelligent robot-assisted therapy were more consistent with robotic control than with manual therapy, reflecting altered movement strategies.

These findings demonstrate that context-aware, intelligent control in rehabilitation exoskeletons can promote active patient participation, reduce compensatory behaviors, and maintain physiologically meaningful muscle engagement. The proposed approach exceeds the results of recent similar studies, being a promising step toward effective, minimally supervised, and task-oriented rehabilitation.