SCH: INT: Collaborative Research: Replicating Clinic Physical Therapy at Home: Touch, Depth, and Epidermal Electronics in an Interactive Avatar System

SCH：INT：合作研究：在家复制临床物理治疗：交互式化身系统中的触摸、深度和表皮电子

• 批准号: 1522125
• 负责人: Pamela Cosman
• 金额: $ 177.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1522125&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Replicating

Physical therapy is often hampered by lack of access to therapists, and lack of adherence to home therapy regimens. This research develops a physical therapy assistance system for home use, with emphasis on stroke rehabilitation. As a person exercises, inexpensive cameras observe color and depth, and unobtrusive tattoo sensors monitor detailed muscle activity. The 3D movement trajectory is derived and compared against the exercise done with an expert therapist. The patient watches a screen avatar where arrows and color coding guide the patient to move correctly. In addition to advancing fields such as movement tracking, skin sensors, and assistive systems, the project has the potential for broad impact by attracting women and under-represented minorities to engineering through health-related engineering coursework and projects, and because home physical therapy assistance can especially help rural and under-served populations. This project uses bio-electronics, computer vision, computer gaming, high-dimensional machine learning, and human factors to develop a home physical therapy assistance system. During home exercises, patient kinematics and physiology are monitored with a Kinect color/depth camera and wireless epidermal electronics transferable to the skin with a temporary tattoo. The project involves optimization of electrode design and wireless signaling for epidermal electronics to monitor spatiotemporal aspects of muscle recruitment, hand and body pose estimation and tracking algorithms that are robust to rapid motion and occlusions, and development of machine learning and avatar rendering algorithms for multi-modal sensor fusion and expert-trained optimal control guidance logic, for both cloud and local usage. The system aims to provide real-time feedback to make home sessions as effective as office visits with an expert therapist, reducing the time and money required for full recovery.

物理治疗往往因缺乏治疗师和缺乏对家庭治疗方案的坚持而受到阻碍。 本研究开发一种家用物理治疗辅助系统，重点是中风康复。 当一个人锻炼时，廉价的相机会观察颜色和深度，而不显眼的纹身传感器会监测详细的肌肉活动。 3D运动轨迹的推导和与专家治疗师完成的运动进行比较。 患者观看屏幕化身，其中箭头和颜色编码引导患者正确移动。 除了推进运动跟踪、皮肤传感器和辅助系统等领域外，该项目还具有广泛影响的潜力，通过与健康有关的工程课程和项目吸引妇女和代表性不足的少数民族参与工程，因为家庭物理治疗援助特别有助于农村和服务不足的人口。该项目使用生物电子学，计算机视觉，计算机游戏，高维机器学习和人为因素来开发家庭物理治疗辅助系统。 在家庭锻炼期间，患者的运动学和生理学通过Kinect彩色/深度摄像头和无线表皮电子设备进行监测，这些电子设备可通过临时纹身转移到皮肤上。该项目涉及优化电极设计和表皮电子设备的无线信令，以监测肌肉招募的时空方面，对快速运动和遮挡具有鲁棒性的手部和身体姿势估计和跟踪算法，以及开发用于多模态传感器融合的机器学习和化身渲染算法和专家训练的最佳控制指导逻辑，用于云和本地使用。 该系统旨在提供实时反馈，使家庭会议与专家治疗师的办公室访问一样有效，减少完全康复所需的时间和金钱。