Actively Recruiting
Smart Home-Based Upper Limb Rehabilitation Using Robot and Task Training for Adults with Stroke and Spinal Cord Injuries
Led by National Cheng-Kung University Hospital · Updated on 2024-01-22
90
Participants Needed
2
Research Sites
30 weeks
Total Duration
AI-Summary
What this Trial Is About
Researchers are evaluating the effectiveness of a smart upper limb rehabilitation system combined with exoskeleton robot devices and an Internet of Things IoT application for home-based training in patients recovering from stroke and those with spinal cord injuries. The study aims to improve motor function, daily living activities, and quality of life by remotely monitoring rehabilitation progress and assisting patient movements. Participants are adults aged 20 to 79 years with specific cognitive and physical criteria depending on their condition. Participants are randomly assigned to one of two groups the TIGER group, which uses a tenodesis-induced-grip exoskeleton robot system, or the TOT group, which receives task-oriented training. Each group follows a home-based training program for 4 weeks. For stroke patients, training is 30 minutes twice daily, 5 days a week for spinal cord injury patients, training is 40 minutes once daily, 5 days a week. The TIGER system offers passive range of motion warm-up and functional training, while the TOT group practices sensorimotor techniques and functional tasks. Participants will undergo assessments before and after the intervention, including motor function tests like the Fugl-Meyer Assessment and Box and Block Test, range of motion measurements, and quality of life evaluations. Researchers will monitor progress in motor activity, independence, and satisfaction with assistive technology. The total study duration spans from baseline through 4 weeks of training, with outcome measures collected at baseline and after the intervention period.
CONDITIONS
Brief Title
A Feasible Smart Upper Limb Rehabilitation Model for Patients With Stroke and Spinal Cord Injuries
Research Team
L
Li-Chieh Kuo, Ph.D
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