Rehabilitation Using Community-Based Affordable Robotic Exercise Systems (Rehab CARES)

项目摘要 中风是导致严重长期残疾的主要原因。据估计，目前有580万至650万人 在美国，患有中风相关残疾的人数将增加，到2030年，这一数字将增加20.5%。当前美国 卫生基础设施没有为这些不断增加的人数做好准备。健康保险覆盖面的局限性， 康复从业人员的短缺减少了获得康复服务的机会。基于社区的设置是 成为提供长期中风后护理的可行场所，然而，他们受到工作人员的困扰， 在这方面，残疾人问题仍然存在，包括缺乏专业知识、治疗师人数少以及缺乏康复资金。因此，质量 护理受到损害，患者的功能结果不等于医院康复 设置.我们寻求开发一种新的解决方案来解决这个问题。实施经济实惠的设计是 提高患者获得康复技术的机会的战略，不论其社会经济地位如何。 这样做可以减少医疗差距，降低长期医疗成本。我们建议使用 负担得起的机器人，以改善在低资源，社区为基础的设置获得高质量的康复护理。在 第一阶段，我们利用1度自由触觉机器人与控制算法开发的测试版 机器人硬件和软件。新的机器人有一个新颖的末端执行器，以允许更多样化的手臂和手 运动，连接到基于云的游戏，并提供针对患者的治疗， 损伤和认知损伤。15名运动障碍程度不同的中风患者将 完成运动和认知障碍的临床评估，然后进行基于机器人的评估， 治疗游戏受试者将配备传感器，监测关键上肢肌肉活动、躯干 机器人任务期间的活动和心率。一个关键的里程碑将是确定机器人的运动学指标 这些任务与运动和认知障碍的临床评分密切相关并可预测临床评分。另一个里程碑将 通过调整机器人的控制参数和游戏参数来驱动患者特定的策略。在 第二阶段，我们将开发硬件，让三个触觉机器人对接（健身房），并配置为允许 患者可以单独或合作玩治疗游戏。我们将测试健身房的安全性和可行性， 基于社区的康复环境，中风患者通常接受每次1小时的物理治疗 (PT)、作业疗法（OT）和言语疗法（SLP）。36名患者将被随机分配至机器人（RT） 或对照组（CT）。两组都将接受PT和SLP，但RT将接受机器人健身房治疗 瞄准上肢和CT将接受剂量匹配的OT小时。治疗将持续4周 两次后续评估。关键里程碑将表明RT具有相同或更好的功能 结果、动机和不良事件作为CT。此外，为了表明机器人健身房是一个具有成本效益的解决方案， 在资源匮乏的社区环境中增加获得高质量康复护理的机会。成功将 验证这种潜在的解决方案，通过用户反馈和更大规模的临床试验证明设计变更的合理性。

PROJECT SUMMARY Stroke is the leading cause of serious long-term disability. It is estimated that 5.8–6.5 million people currently live with stroke related disability in the US and that this number will increase by 20.5% by 2030. The current US health infrastructure is not prepared for these increasing numbers. Limitations in health insurance coverage and the shortage of rehabilitation practitioners decrease access to rehabilitation. Community-based settings are becoming viable venues for delivering long-term post-stroke care, however, they are plagued by staff with limited expertise, low number of therapists and lack of financial resources for rehabilitation. Because of this, the quality of care is compromised, and functional outcomes of patients are not equal to hospital-based rehabilitation settings. We seek to develop a novel solution to this problem. Implementing affordable design is a fundamental strategy for increasing access to rehabilitation technology for patients regardless of socio-economic status. Doing so, decreases healthcare disparities and reduces long-term healthcare costs. We propose to use affordable robots to improve access to quality rehabilitation care in low-resource, community-based settings. In Phase 1, we leverage a 1 degree of freedom haptic robot with control algorithms to develop a beta version of the robot hardware and software. The new robot have a novel end-effector to allow more diverse arm and hand exercises, be connected to cloud-based gaming, and provide patient-specific therapy that adjusts for motor impairment and cognitive impairment. 15 stroke patients with a wide range of motor impairment levels will complete clinical assessments of motor and cognitive impairment followed by robot-based assessment and therapy games. Subjects will be instrumented with sensors monitoring key upper extremity muscle activity, trunk activity and heart rate during robot tasks. A key milestone will be to identify kinematic metrics from the robot tasks that strongly correlate and predict clinical scores of motor and cognitive impairment. Another milestone will to drive patient-specific strategies by adjusting the robot’s control parameters and the game parameters. In Phase 2, we will develop the hardware to allow three haptic robots to dock (a gym) and be configured to allow patients to play therapy games alone or collaboratively. We will test the safety and feasibility of the gym in a community-based rehabilitation setting where stroke patients typically receive 1 hour each of physical therapy (PT), occupational therapy (OT) and speech therapy (SLP). 36 patients will be randomized to either a robot (RT) or a control group (CT). Both groups will receive PT and SLP, but the RT will receive the robot gym therapy targeting the upper limb and the CT will receive a dose-matched hour of OT. Therapy will occur over 4 weeks with two follow-up assessments. Key milestones will be to show that the RT has the same or better functional outcomes, motivation, and adverse events as the CT. Also, to show that the robot gym is a cost-effective solution to increasing access to quality rehabilitation care in low-resource, community-based settings. Success here will validate this potential solution, justify design changes revealed via user-feedback and a larger clinical trial.