Publisher

UMBUMB

Journal SINERGISINERGI Abstract

This research introduces a two-degree-of-freedom rehabilitation robotic platform to enhance Constraint-Induced Movement Therapy (CIMT) for post-stroke upper limb rehabilitation. Unlike conventional CIMT, that depends on therapist intervention, the proposed system integrates a control framework balancing assistance and autonomy to improve patient engagement and recovery efficiency. The main contribution is a hybrid control architecture combining a low-level impedance controller with a high-level discrete event system (DES) controller. This dual-layer control enables real-time adaptation to a patients motor impairment stage, offering dynamic and personalized rehabilitation. The high-level controller, structured around the Chedoke-McMaster Assessment (CMA), facilitates intelligent transitions between rehabilitation states, ensuring robotic assistance matches recovery progress. The design emphasizes simplicity, portability, and user-friendliness, employing a lightweight, cable-driven mechanism that produces smooth and natural movements, closely replicating manual therapy. Experiments with healthy subjects simulating impaired conditions demonstrated the systems ability to adjust assistance levels and movement velocities according to motor function stages. The results confirm the feasibility of an adaptive, patient-centric control framework that enhances motor engagement and supports progressive rehabilitation. Future work will focus on clinical validation with stroke patients, expanding movement directions, and long-term evaluation of therapeutic outcomes in real-world settings. Overall, this study offers a scalable, data-driven approach bridging robotic automation and therapist-guided rehabilitation, opening new possibilities for improving neuroplasticity and motor recovery after stroke.

Conclusion

This study successfully demonstrates the integration of a high-level supervisory control framework with precise plant inputs in a robot-assisted rehabilitation system, significantly enhancing patient engagement and recovery outcomes.The systems key innovation lies in the discrete event system (DES)-based high-level controller, structured around the Chedoke-McMaster Assessment (CMA), enabling stage-specific adaptation of therapy based on patient motor impairment.The lightweight, cable-driven mechanical design closely mimics therapist-guided movements while maintaining portability and ease of use, and experimental validation confirms the systems ability to deliver adaptive, personalized rehabilitation.

Future Research

Further research should investigate the systems efficacy with stroke patients in clinical settings to validate its therapeutic benefits and refine its parameters for optimal recovery. Expanding the robots range of motion beyond the current planar movements to include multi-directional capabilities would broaden its applicability to a wider range of rehabilitation exercises and patient needs. Additionally, exploring the integration of machine learning algorithms to personalize therapy protocols based on individual patient data and predict recovery trajectories could further enhance the systems adaptive capabilities and optimize treatment outcomes, ultimately leading to more effective and efficient rehabilitation strategies for stroke survivors. These advancements will require a collaborative effort between engineers, clinicians, and neuroscientists to ensure the development of a truly patient-centric and data-driven rehabilitation system.

References

1. Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control - Nor... journals.sagepub.com/doi/10.5772/56666Neuro Based Thumb Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control Nor journals sagepub doi 10 5772 56666

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