SCH: EXP: Collaborative Research: Hierarchical Capacitive Sensing for Environmental Control and Physical Therapy for Patients with Paralysis

SCH：EXP：合作研究：用于瘫痪患者环境控制和物理治疗的分层电容传感

• 批准号: 1406626
• 负责人: Nilanjan Banerjee
• 金额: $ 50.27万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406626&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Hierarchical

An estimated 1.5 million individuals in the United States are hospitalized each year because of strokes, brain injuries and spinal cord injuries. Severe impairment such as paralysis, paresis, weakness and limited range of motion are common sequels resulting from these injuries, requiring extensive rehabilitation. This project is developing invisible sensing systems embedded into bed sheets, pillows, wheelchair pads, and clothing, for environmental control and physical therapy for such paralysis patients. The system detects gestures regardless of evolving environmental and patient conditions and provides explicit real-time feedback to the user. Through the use of low-cost and ultra-low power capacitive sensing, the system reduces hospital visits and therapy costs. The proposed system addresses the limitations of existing assistive care sensors through three novel technical contributions: (1) The use of a self-sustainable hierarchy of sensors; textile-based capacitive sensor arrays (CSA) and inertial sensors on the human body; to improve the accuracy of gesture recognition while consuming minimal energy. The inertial sensors train the capacitive sensor arrays for different body positions; (2) A self-learning algorithm that determines gestures automatically regardless of the position of the patient's body and conditions using templates of gestures and patient conditions over time; and (3) Seamless integration of the patient in the feedback loop using amplification and animation to provide explicit real-time feedback to the user on how she/he is performing on his/her physical therapy, and how the system is interpreting his/her gestures. Additionally, the PIs are developing a cross-disciplinary undergraduate and graduate course that focuses on developing sensing systems while being cognizant of the actual needs in a rehabilitation hospital. The PIs are also using local university initiatives to engage minority and women researchers in the project.

据估计，美国每年有150万人因中风、脑损伤和脊髓损伤住院。严重的损伤，如瘫痪，轻瘫，虚弱和活动范围有限是这些损伤的常见后遗症，需要广泛的康复。该项目正在开发嵌入床单，枕头，轮椅垫和衣服的隐形传感系统，用于环境控制和对此类瘫痪患者进行物理治疗。无论环境和患者状况如何变化，该系统都能检测手势，并向用户提供明确的实时反馈。通过使用低成本和超低功耗的电容传感，该系统减少了医院就诊和治疗成本。所提出的系统通过三个新的技术贡献解决了现有辅助护理传感器的局限性：（1）使用自我可持续的传感器层次结构;基于纺织品的电容传感器阵列（CSA）和人体上的惯性传感器;以提高手势识别的准确性，同时消耗最少的能量。惯性传感器针对不同的身体位置训练电容传感器阵列;（2）自学习算法，其使用手势和患者状况的模板随时间自动确定手势，而不管患者身体的位置和状况如何;以及（3）使用放大和动画将患者无缝地集成在反馈回路中，以向用户提供关于她/她如何使用反馈回路的明确的实时反馈。他正在进行他/她的物理治疗，以及系统如何解释他/她的手势。此外，PI正在开发一个跨学科的本科生和研究生课程，重点是开发传感系统，同时认识到康复医院的实际需求。PI还利用当地大学的倡议，让少数民族和妇女研究人员参与该项目。