SBIR Phase II: A machine learning-driven telerehabilitation solution designed to promote the personalized recovery of hand and arm functions post stroke

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是，通过远程康复医疗设备，潜在地改善中风手臂和手损伤患者的生活质量。在美国，每年约有80万人中风，其中约65%的人患有长期的上肢损伤。由于成本、交通和时间等诸多障碍，许多患者得不到足够的康复治疗。远程康复方法可能会减少其中的一些障碍，允许治疗师和他们的患者进行有意义的远程治疗。对于治疗师来说，这可能会通过自动化流程来审查患者的进展，调整他们的康复治疗，并为服务收费，从而改善财务结果。该项目将推动为中风患者开发专门用于手和手臂运动恢复的个性化远程康复系统。为手指、手和手臂的康复而设计的新的运动游戏将被开发并添加到当前的游戏库中。机器学习将被添加到系统中，以创建一个多功能、引人入胜和可定制的解决方案。这种新的康复方法将通过预测性分析解决个人用户需求，从而使治疗个性化，可能会更有效。机器学习将驱动推荐系统使康复计划与患者的康复轨迹同步。这种同步将帮助治疗师提供个性化的治疗练习，并可能提高患者的康复结果。这些游戏和机器学习算法将与临床医生和中风患者一起进行评估。最后一步是在一个综合性的中风中心测试该系统的可行性。这些个性化的虚拟康复、远程临床医生监督和进度跟踪能力可能提供一种经济高效的方式来改善患者的结果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to potentially improve the quality of life for individuals suffering arm and hand impairments from stroke, through a medical device for telerehabilitation. Each year, ~800,000 people have a stroke in the United States, and about 65% of them suffer long-term upper extremity impairments. Due to many barriers such as cost, transportation, and time, many individuals do not obtain enough therapy for recovery. The telerehabilitation approach may reduce some of these barriers, allowing therapists and their patients to have meaningful remote sessions. For therapists, this may improve fiscal outcomes by automating the flow of reviewing patient progress, adjusting their rehabilitation treatments, and billing for services. This project will advance the development of a personalized telerehabilitation system, specifically for hand and arm motor recovery, for individuals suffering from a stroke. New exergames designed for rehabilitation of the fingers, hand, and arm will be developed and added to the current library of games. Machine learning will be added to the system to create a versatile, engaging, and customizable solution. This novel approach to rehabilitation will personalize treatments that may be more effective by addressing individual user needs with predictive analytics. Machine learning will drive the recommendation system to synchronize the rehabilitation plan with the patient recovery trajectory. This synchronization will help the therapist provide personalized therapeutic exercises and possibly increase their patients’ recovery outcomes. The games and machine learning algorithms will be evaluated with clinicians and individuals with stroke. The final step will be to test the feasibility of the system in a comprehensive stroke center. These capabilities of personalized virtual rehabilitation, remote clinician supervision, and progress tracking may offer a cost-effective way to improve patient outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.