Comprehensive upper-limb human-machine interaction strategies for post-stroke upper-limb rehabilitation

Background Stroke and its related complications, place significant burdens on human society in the 21st century, and lead to substantial demands for rehabilitation. To fulfill rehabilitation needs, human-machine interaction (HMI) technology strives continuously. Traditional passive HMI strategy requires device to be dynamically well-fitting, and executes command precisely. Advanced active HMI strategy also demands the device to react fast and accurately based on user’s intention. However, current study usually focuses on limited aspect of HMI, a complete HMI study which addresses the complexity of stroke related complications and provide the possibility for personalized post-stroke upper-limb rehabilitation is lacking. Method An Up-limb Rehabilitation Device and Utility System (UarDus) is proposed along with 3 HMI strategies namely robot-in-charge, therapist-in-charge and patient-in-charge. Based on physiological structure of human upper-limb and scapulohumeral rhythm (SHR) of shoulder, a base exoskeleton with 14 degrees of freedoms (DoFs) is designed as foundation of the 3 strategies. Passive robot-in-charge and therapist-in-charge strategies provides fully-assisted rehabilitation options. The active patient-in-charge strategy incorporates data acquisition matrices and a new deep learning model, which is developed based on CNN and Transformer structure, aims to provide partially-assisted rehabilitation. Results Kinematically, the work space of the base exoskeleton is presented first. Utilizing motion capture technology, the GH center of both human and exoskeleton is compared the well-matched curves suggesting comfortable dynamic wear experience. For robot-in-charge and therapist-in-charge strategy, the desired and measured angle-time curve present good correlation, with low phase difference, which serve the purpose of real-time control. Featuring the patient-in-charge strategy, Kernel Density Estimation (KDE) result suggesting reasonable sensor-machine-human synergy. Applying K-fold (K = 10) cross-validation method, the classification accuracy of the proposed model achieves an average of 99.6% for the designated 15 actions. The proposed model is also evaluated on public datasets, demonstrating outstanding response time and accuracy comparing with state-of-art models. Conclusions A validated exoskeleton hardware system named UarDus is constructed, along with the 3 HMI strategies proposed. This work provides possibility for people with stroke to engage in complex personalized rehabilitation training with dynamic comfortable wear experience.