Cloud-based rehabilitation services for post-stroke hand disability.
Tensor-based pattern recognition technique to detect the real-time condition of patient.
The integration of cloud computing with AR-based rehabilitation system.
Multi-sensory big data oriented tensor approach to handle patient’s collected data.
Given the flexibility and potential of cloud technologies, cloud-based rehabilitation frameworks have shown encouraging results as assistive tools for post-stroke disability rehabilitation exercises and treatment. To treat post-stroke disability, cloud-based rehabilitation offers great advantages over conventional, clinic-based rehabilitation, providing ubiquitous flexible rehabilitation services and storage while offering therapeutic feedback from a therapist in real-time during patients’ rehabilitative movements. With the development of sensory technologies, cloud computing technology integrated with Augmented Reality (AR) may make therapeutic exercises more enjoyable.
To achieve these objectives, this paper proposes a framework for cloud-based rehabilitation services, which uses AR technology along with other sensory technologies. We have designed a prototype of the framework that uses the mechanism of sensor gloves to recognize gestures, detecting the real-time condition of a patient doing rehabilitative exercises. This prototype framework is tested on twelve patients not using sensor gloves and on four patients wearing sensor gloves over six weeks. We found statistically significant differences between the forces exerted by patients’ fingers at week one compared to week six. Significant improvements in finger strength were found after six weeks of therapeutic rehabilitative exercises.