CPS: Cyber-physically assistive clothing to reduce societal incident of low back pain

CPS：网络物理辅助服装可减少腰痛的社会事件

基本信息

批准号：
9979852
负责人：
Karl E Zelik
金额：
$ 31.16万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-24 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9979852
关键词：
Address Algorithms Back Back Injuries Back Pain Biological Biomechanics Clinical Clinical Management Clothing Cumulative Trauma Disorders Data Data Set Ensure Environment Feedback Health Healthcare High Prevalence Human Human body Incidence Individual Injury Intervention Lead Life Lifting Low Back Pain Machine Learning Methods Modeling Monitor Movement Muscle Musculoskeletal Occupations Pain Pattern Physical activity Public Health Research Risk Factors Robotics Safety Self-Help Devices System Techniques Technology Time Tissues Training Vertebral column Vision Work Workplace base cost cyber physical data sharing design evidence base exoskeleton experimental study health data human subject human-in-the-loop learning strategy light weight multidisciplinary multimodality operation physically handicapped portability prevent prototype sensor wearable sensor technology

项目摘要

The objective of this proposal is to address core scientific challenges related to sensing, actuation and control of cyber-physically assistive clothing (CPAC). CPAC is a kind of Human-in-the-loop Cyber-Physical System (HCPS), in which actuated clothing is coordinated in unison with human body movement to enhance safety and health. We propose addressing key HCPS challenges within the context of using CPAC to reduce societal incidence of low back pain, by preventing lumbar (spine) overloading and overuse injuries. Low back pain is targeted because it is one of the leading causes of physical disability and missed work. High and/or repetitive forces on lumbar muscles and discs can occur during daily tasks, and are known to be major risk factors that can lead to back pain and injury. The long-term vision is to create smart clothing that can monitor lumbar loading, train safe movement patterns, and directly assist wearers to reduce the musculoskeletal forces that cause pain and injury. This proposed transformation of clothing is similar to how wristwatches have transformed from timepieces into health monitors; however, CPAC is even more exciting because it combines the form-factor of clothing with the assistance benefits of an exoskeleton to reduce biological tissue loading for a broad range of individuals, occupations and tasks. Thrust 1 will adapt machine learning techniques in order to monitor lumbar loading and detect excessive spine forces via portable, wearable sensors, such that timely feedback/intervention can be provided. This thrust will result in the creation of a publicly shared data set that contains synchronized, multimodal (lab-based and wearable) sensor data collected from >500 actions per subject, the largest such corpus for machine learning in this domain. Thrust 2 will model the dynamics of cyber, physical and human components of CPAC in order to develop optimal control and learning strategies. Thrust 3 will integrate sensors, fusion algorithms and portable actuation into a complete wearable prototype. A human subject experiment will be performed to objectively evaluate the function of CPAC. At the focus of this proposal is the human body; monitored, analyzed and assisted by multidisciplinary CPS technologies. The project integrates expertise in biomechanics, machine learning, sensor fusion, soft robotics, wearable assistive technology, and clinical management of low back pain to transform clothing from materials that cover the body into wearable systems that can track and protect low back health. The key scientific HCPS challenges that need to be overcome, and which are addressed in this proposed research, in order to realize the broad societal benefits of CPAC are: (1) real-time sensing and assistive control of the HCPS and its co-adaptation to different subjects and diverse environments, (2) system design and verification ensuring safe operation and that no harm is done to human subjects through unanticipated feedback, (3) selection and placement of low cost sensors aiding affordable and realistic manufacturing of CPAC, (4) integration of wearable sensors and actuators into a reliable and effective HCPS.

该提案的目标是解决与传感、驱动和控制相关的核心科学挑战网络物理辅助服装（CPAC）。 CPAC是一种人机交互信息物理系统（HCPS），它驱动的衣服与人体运动协调一致，以提高安全和健康。我们建议在使用 CPAC 降低腰背社会发病率的背景下解决 HCPS 的关键挑战通过防止腰椎（脊柱）超负荷和过度使用损伤来缓解疼痛。腰痛是有针对性的，因为它是造成身体残疾和缺勤的主要原因。对腰部肌肉和椎间盘的高强度和/或重复性作用力可能会导致发生在日常工作中，并且被认为是可能导致背痛和受伤的主要危险因素。长期来看愿景是创造智能服装，可以监测腰部负荷，训练安全运动模式，并直接协助佩戴者可以减少导致疼痛和伤害的肌肉骨骼力量。此次提出的服装改造是类似于腕表从计时器转变为健康监测器；然而，CPAC 更令人兴奋的是，它结合了服装的外形因素和外骨骼的辅助优势，以减少适用于各种个人、职业和任务的生物组织负载。 Thrust 1 将适应机器学习通过便携式可穿戴传感器监测腰部负载并检测过度脊柱力的技术，例如可以提供及时的反馈/干预。这一推动力将导致创建一个公开共享的数据集包含从每个超过 500 个操作收集的同步、多模式（基于实验室和可穿戴）传感器数据主题，该领域最大的机器学习语料库。 Thrust 2 将模拟网络的动态， CPAC 的物理和人体成分，以制定最佳控制和学习策略。推力3将将传感器、融合算法和便携式驱动集成到完整的可穿戴原型中。人类主体将进行实验来客观评价CPAC的功能。该提案的重点是人类身体;由多学科 CPS 技术进行监控、分析和协助。该项目整合了以下方面的专业知识生物力学、机器学习、传感器融合、软机器人、可穿戴辅助技术和临床管理将衣服从覆盖身体的材料转变为可以跟踪和记录的可穿戴系统保护腰部健康。需要克服并解决的关键科学 HCPS 挑战本研究旨在实现 CPAC 的广泛社会效益：（1）实时传感和辅助 HCPS的控制及其对不同主体和不同环境的适应性，（2）系统设计和验证确保安全操作，并且不会因意外反馈而对人类受试者造成伤害，(3) 选择和放置低成本传感器，有助于经济实惠且现实的 CPAC 制造，(4) 集成将可穿戴传感器和执行器集成到可靠且有效的 HCPS 中。