Development, Evaluation and Translation of Robotic Apparel for Alleviating Low Back Pain

用于缓解腰痛的机器人服装的开发、评估和转化

• 批准号: 9898052
• 负责人: Conor Walsh
• 金额: $ 112.38万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-26 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9898052
• 关键词: Accounting; Achievement; Acute; Affective; Agriculture; Algorithms; Back; Back Injuries; Back Pain; Behavior; Biofeedback; Biomechanics; Biophysics; Chronic low back pain; Clinical; Cognition; Collaborations; Communities; Conditioned Reflex; Data; Development; Distress; Effectiveness; Emergency department visit; Engineering; Enhancement Technology; Environment; Evaluation; Event; Exercise; Exertion; Exposure to; Fatigue; Foundations; Frequencies; Fright; Goals; Healthcare; Hip Joint; Hip region structure; Human; Hyperactive behavior; Individual; Industry; Injury; Intuition; Knowledge; Laboratories; Lead; Life; Lifting; Logistics; Low Back Pain; Lower Back Injury; Manuals; Modeling; Movement; Muscle; Muscle Fatigue; Musculoskeletal Diseases; Nature; Neurorehabilitation; Nociception; Occupational; Occupations; Outcome; Pain; Patients; Phase; Physiological; Posture; Prevention; Process; Randomized Controlled Trials; Recording of previous events; Recovery; Recurrence; Rehabilitation therapy; Reporting; Research; Research Personnel; Risk; Risk Factors; Robot; Robotics; Site; Strenuous Exercise; Study Subject; System; Technology; Training; Translating; Translations; Work; Workplace; assistive robot; avoidance behavior; base; biopsychosocial; clinical translation; coping mechanism; deconditioning; design; disability; human subject; human-in-the-loop; mechanical load; motor control; next generation; physical therapist; prevent; programs; robotic system; technology development; tool

PROJECT SUMMARY/ABSTRACT For many individuals working in industries such as construction, manufacturing, logistics, healthcare, and agriculture, a typical workday involves exposure to frequent overexertion activities (repetitive bending, twisting, lifting, and sustained flexion postures) that increase the risk of back injury. Overexertion is the most common event leading to musculoskeletal disorders (MSDs)—over 65% of MSDs are associated with overexertion, and one-third of all MSDs resulting in days away from work are accounted for by disabling occupational chronic low back pain (cLBP). A majority of back injuries are considered non-specific, as an exact nociceptive cause is often impossible to identify. Among biophysical risk factors, studies have shown that cumulative and peak loads on the back are predictors of low back pain. Lifting is a dynamic and highly variable activity and is the most commonly reported event leading to low back pain, accounting for nearly one-third of all back pain emergency room visits. Lifting with awkward poses (asymmetrical lifting, large horizontal or vertical load distances), at a high frequency, or performing multiple repetitions can result in an increased risk of injury due to increased peak and cumulative mechanical loading of the back and fatigue effects. A primary factor contributing to acute or recurrent back injury is overexertion via excessive peak and cumulative forces on the back, and that primary factors involved in the progression of acute low back injury to cLBP include maladaptive motor control strategies, muscle hyperactivity, reduced movement variability, and the development of fear cognitions. This project will focus on the development of robotic apparel with integrated biofeedback components can reduce exertion, encourage safe, varied movement strategies, and promote recovery. Robotic apparel will be capable of providing supportive forces to the back and hip joints when needed via adaptive control algorithms that respond to dynamic movements, and become fully transparent when assistance is no longer needed. This technology can may be able to be used to prevent cLBP in individuals who are exposed to overexertion; in this way, robotic apparel will supplement ergonomic training implemented in many manual materials handling occupations to encourage proper lifting strategies in the workplace. Robotic apparel will also provide a new tool to physical therapists and the clinical community to enhance rehabilitation programs by assisting in the safe progression back to normal activity, enabling people to get back to work sooner while encouraging non-maladaptive movement strategies. This research will undertake a staged approach and be based on a human-in-the-loop development process that evaluates component and system functions through frequent human subject studies, where quantitative (robot, biomechanical and physiological) as well as qualitative (i.e., human factors) data will be collected.

项目摘要/摘要 对于许多从事建筑，制造，物流，医疗保健和 农业是一个典型的工作日 提起和持续的屈曲姿势）增加了背部受伤的风险。过度劳累是最常见的 导致肌肉骨骼疾病（MSDS）的事件 - 超过65％的MSD与劳累有关，并且 通过禁用职业慢性低，可以解决所有导致工作几天的MSD的三分之一 背痛（CLBP）。大多数背部受伤被认为是非特异性的，因为确切的伤害感因原因通常是 在生物物理危险因素中，研究表明，累积和峰值负载 背部是腰痛的预测因子。举起是一种动态且高度可变的活动，是最多的 通常报道的事件导致腰痛，占所有背痛的几乎三分之一紧急情况 客房访问。以尴尬的位置（不对称举重，大水平或垂直载荷距离）举起，在 高频或进行多次重复会导致由于峰值增加而导致受伤的风险增加 背部和疲劳效应的累积机械负荷。导致急性或 反复的背部受伤是通过过量的峰值和背部累积力过度劳累的，而主要的 急性下背部损伤对CLBP进展的因素包括适应不良运动控制策略， 肌肉多动症，运动变异性降低以及恐惧认知的发展。这个项目将 专注于使用集成生物反馈组件的机器人服装的开发可以减少劳累， 鼓励安全，多样化的运动策略并促进康复。机器人服装将能够提供 在需要时通过自适应控制算法在需要时向背部和髋关节的支撑力，这些算法对动态做出反应 运动，并在不再需要援助时变得完全透明。这项技术可以 用于防止暴露于过度劳累的个体中的CLBP；这样，机器人服装将 在许多手动材料处理占用中实施的补充符合人体工程学培训以鼓励 在工作场所适当提起策略。机器人服装还将为物理治疗师和 临床社区通过协助安全进步恢复正常来增强康复计划 活动，使人们能够更快地重新上班，同时鼓励非疟原虫运动策略。 这项研究将采用分阶段的方法，并基于人类的开发过程 通过经常人类主题研究评估组件和系统功能，其中定量（机器人， 将收集生物力学和物理）以及定性（即人为因素）数据。

项目成果

Evaluating adaptiveness of an active back exosuit for dynamic lifting and maximum range of motion.

评估主动式背部外装对于动态提升和最大运动范围的适应性。

• DOI: 10.1080/00140139.2023.2240044
• 发表时间: 2024
• 期刊: Ergonomics
• 影响因子: 2.4
• 作者: Quirk,DAdam;Chung,Jinwon;Applegate,Megan;Cherin,JasonM;Dalton,DianeM;Awad,LouN;Walsh,ConorJ
• 通讯作者: Walsh,ConorJ

Reducing Back Exertion and Improving Confidence of Individuals with Low Back Pain with a Back Exosuit: A Feasibility Study for Use in BACPAC.

• DOI: 10.1093/pm/pnad003
• 发表时间: 2023-08-04
• 期刊: PAIN MEDICINE
• 影响因子: 3.1
• 作者: Quirk, D. Adam;Chung, Jinwon;Schiller, Gregory;Cherin, Jason M.;Arens, Philipp;Sherman, David A.;Zeligson, Emma R.;Dalton, Diane M.;Awad, Lou N.;Walsh, Conor J.
• 通讯作者: Walsh, Conor J.