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.

该提案的目的是解决与感应，驱动和控制有关的核心科学挑战网络物理辅助服装（CPAC）。 CPAC是一种人类在循环网络物理系统（HCP）中，驱动的衣服与人体运动一致协调，以增强安全性和健康状况。我们提出在使用CPAC降低社会发病率的背景下解决关键HCP的挑战疼痛，防止腰部（脊柱）超负荷和过度使用伤害。腰痛是针对性的，因为它是主要原因是身体残疾和缺席的工作。腰部肌肉和椎间盘上的高和/或重复力可以发生在日常任务期间，众所周知是可能导致背部疼痛和受伤的主要危险因素。长期视觉是创造智能服装，可以监测腰部负荷，训练安全运动模式并直接协助佩戴者减少引起疼痛和损伤的肌肉骨骼力。这项提议的衣服转变是类似于手表从钟表转变为健康监测器的方式；但是，CPAC甚至更多令人兴奋，因为它将服装的形式与外骨骼的帮助相结合，以减少为广泛的个体，职业和任务的生物组织加载。推力1将适应机器学习技术以监测腰部载荷并通过便携式可穿戴传感器检测过多的脊柱力，例如可以提供及时的反馈/干预。这种推力将导致创建公开共享的数据集其中包含从> 500个操作收集的同步，多模式（基于实验室和可穿戴的）传感器数据主题是该领域中机器学习的最大语料库。推力2将模拟网络的动力学， CPAC的物理和人类组成部分，以制定最佳的控制和学习策略。推力3将将传感器，融合算法和便携式启动集成到完整的可穿戴原型中。人类主题实验将进行客观评估CPAC的功能。该提议的重点是人类身体;通过多学科CPS技术对监测，分析和协助。该项目将专业知识纳入生物力学，机器学习，传感器融合，软机器人技术，可穿戴辅助技术和临床管理下腰痛以使衣服从覆盖人体的材料转变为可穿戴系统的材料，这些系统可以追踪和保护下背部健康。需要克服的关键科学HCP挑战，并在为了意识到CPAC的广泛社会利益，这项拟议的研究是：（1）实时感知和辅助控制HCP及其对不同主题和不同环境的共同适应，（2）系统设计和验证确保安全操作，并且通过意外反馈对人类受试者无害，（3）低成本传感器的选择和放置协助CPAC负担得起且现实的制造，（4）集成可穿戴的传感器和执行器变成可靠且有效的HCP。