Enabling Independent Living for Individuals with Cervical Spinal Cord Injury via High-Density Electromyography Controlled Robotic Systems

通过高密度肌电图控制的机器人系统使颈脊髓损伤患者能够独立生活

• 批准号: 2341352
• 负责人: Zackory Erickson
• 金额: $ 59.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341352&HistoricalAwards=false
• 关键词: Enabling; Independent; Living; Individuals; Cervical

In the United States, there are over 100,000 individuals living with a spinal cord injury who have lost the use of their hands. The loss of hand function can dramatically change a person’s daily life, forcing them to rely on caregivers to provide assistance. This project aims to develop sensor-embedded garments designed to monitor muscle activity in individuals with tetraplegia resulting from spinal cord injuries. This sensorized garment detects muscle activity in the upper body, even for many people with spinal cord injuries who have lost hand mobility. By decoding and translating these muscle activities, the garment empowers individuals to take control of an assistive robot to perform a variety of household and self-care tasks, such as preparing and eating meals independently and conducting household chores. This research is poised to impact society by restoring a degree of autonomy and agency to individuals with spinal cord injury, promoting open-source development in wearable sensing, integrating research findings into classroom materials, and fostering inclusive education and mentorship for high school students from communities underserved in STEM.This collaborative project between Carnegie Mellon University and the University of Pittsburgh will introduce a high-density electromyography (HDEMG) wearable to capture neuromuscular signals and enable individuals with spinal cord injury (SCI) to embody a physically assistive mobile manipulator. Towards this goal, the research project will make key foundational contributions along three thrusts: (1) The design of a sensorized upper-body garment embedded with HDEMG arrays, allowing the capture of residual myoelectric activity and recognition of gesture intent in individuals with SCI. The research team will develop learning-based pattern recognition techniques to interpret the myoelectric activity patterns of attempted arm, wrist, and hand gestures and predict the underlying motor function intent. (2) Development of an interface that enables individuals with SCI to embody an assistive robot by mapping myoelectric signals and inferred gestures to actuator control. The sensorized garment is designed to be comfortable and wearable throughout an entire day, which also necessitates the development of real-time calibration algorithms that allow for accurate myoelectric activity sensing over extended periods of time. (3) The team will conduct a series of in-lab evaluations with stakeholders in collaboration with our community partners. Individuals with SCI will use the wearable to teleoperate an assistive mobile manipulator and perform a range of physical household and self-care activities. These studies will quantify technology performance and efficacy, assess safety, and support iterative refinement and improvements to the key technology components with stakeholder feedback.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.

在美国，有超过100，000人患有脊髓损伤，他们失去了双手的使用。手功能的丧失会极大地改变一个人的日常生活，迫使他们依赖护理人员提供帮助。该项目旨在开发嵌入传感器的服装，旨在监测脊髓损伤导致四肢瘫痪的个体的肌肉活动。这种传感器服装可以检测上半身的肌肉活动，即使对于许多脊髓损伤的人来说也是如此。通过解码和翻译这些肌肉活动，服装使个人能够控制辅助机器人来执行各种家庭和自我护理任务，例如独立准备和进食以及进行家务。这项研究有望通过恢复脊髓损伤患者一定程度的自主权和代理权来影响社会，促进可穿戴传感的开源开发，将研究成果融入课堂材料，并为来自STEM服务不足社区的高中生提供全纳教育和指导。卡内基梅隆大学和匹兹堡大学之间的这个合作项目将引入一个高-密度肌电图（HDEMG），可穿戴以捕获神经肌肉信号，并使脊髓损伤（SCI）个体能够体现物理辅助移动的操纵器。为了实现这一目标，该研究项目将沿着沿着三个方向做出关键的基础性贡献：（1）设计嵌入HDEMG阵列的传感器上身服装，允许捕获SCI患者的残余肌电活动和识别手势意图。研究团队将开发基于学习的模式识别技术，以解释尝试手臂，手腕和手势的肌电活动模式，并预测潜在的运动功能意图。(2)开发一个接口，使SCI患者能够通过将肌电信号和推断的手势映射到执行器控制来体现辅助机器人。传感器化服装被设计为全天舒适且可穿戴，这也需要开发实时校准算法，以允许在长时间内准确地感测肌电活动。(3)该团队将与我们的社区合作伙伴合作，与利益相关者进行一系列实验室内评估。SCI患者将使用可穿戴设备远程操作辅助移动的机械手，并执行一系列物理家庭和自我护理活动。这些研究将量化技术性能和有效性，评估安全性，并支持迭代改进和改进的关键技术组件与利益相关者的反馈。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。