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

SBIR 第二阶段：机器学习驱动的远程康复解决方案，旨在促进中风后手部和手臂功能的个性化恢复

• 批准号: 2226174
• 负责人: Mee Eriksson
• 金额: $ 99.77万
• 依托单位: NEUROTECHR3 INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226174&HistoricalAwards=false
• 关键词: SBIR; Phase; II; machine; learning

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.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响/商业潜力是通过医疗设备通过医疗设备来改善遭受中风和手动障碍的个人的生活质量。每年，约有80万人在美国中风，其中约65％的上肢障碍长期障碍。由于许多障碍，例如成本，运输和时间，许多人没有获得足够的治疗来恢复。电信方法可以减少其中一些障碍，从而使治疗师及其患者可以进行有意义的远程课程。对于治疗师而言，这可能会通过自动审查患者进度，调整康复治疗和服务账单的流动来改善财政成果。该项目将推动针对患有中风的人的个性化电信系统，特别是用于手和手臂恢复的系统。将开发用于手指，手和手臂康复的新的Exergames，并将添加到当前的游戏库中。机器学习将添加到系统中，以创建一种多功能，引人入胜且可自定义的解决方案。这种新颖的康复方法将通过通过预测分析来满足个人用户需求来个性化治疗方法。机器学习将推动推荐系统，以将康复计划与患者康复轨迹同步。这种同步将有助于治疗师提供个性化的理论练习，并可能增加患者的康复结果。游戏和机器学习算法将与中风的临床医生和个人进行评估。最后一步是在全面的中风中心测试系统的可行性。这些个性化的虚拟康复，远程临床监督和进度跟踪的能力可能提供了一种具有成本效益的方式来改善患者的结果。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来支持的珍贵支持。