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

• 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
• 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

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。

项目成果

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

• DOI: 10.3390/s21020340
• 发表时间: 2021-01-06
• 期刊: Sensors (Basel, Switzerland)
• 作者: Matijevich ES;Volgyesi P;Zelik KE
• 通讯作者: Zelik KE

Exoskeletons and Exosuits Could Benefit from Mode-Switching Body Interfaces That Loosen/Tighten to Improve Thermal Comfort.

• DOI: 10.3390/ijerph182413115
• 发表时间: 2021-12-12
• 期刊: International journal of environmental research and public health
• 作者: Elstub LJ;Fine SJ;Zelik KE
• 通讯作者: Zelik KE

How Accurately Can Wearable Sensors Assess Low Back Disorder Risks during Material Handling? Exploring the Fundamental Capabilities and Limitations of Different Sensor Signals.

• DOI: 10.3390/s23042064
• 发表时间: 2023-02-12
• 期刊: Sensors (Basel, Switzerland)

Tibial bone forces can be monitored using shoe-worn wearable sensors during running.

• DOI: 10.1080/02640414.2022.2107816
• 发表时间: 2022-08
• 期刊: Journal of sports sciences
• 影响因子: 3.4

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

• DOI: 10.1016/j.humov.2020.102690
• 发表时间: 2020-12
• 期刊: HUMAN MOVEMENT SCIENCE
• 影响因子: 2.1
• 作者: Matijevich, Emily S.;Scott, Leon R.;Volgyesi, Peter;Derry, Kendall H.;Zelik, Karl E.
• 通讯作者: Zelik, Karl E.