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.

项目总结/摘要 对于许多在建筑、制造、物流、医疗保健等行业工作的人来说， 农业，典型的工作日涉及暴露于频繁的过度用力活动（重复弯曲，扭曲， 举重和持续的屈曲姿势），这会增加背部受伤的风险。过度劳累是最常见的 导致肌肉骨骼疾病（MSD）的事件-超过65%的MSD与过度用力相关，以及 三分之一的导致缺勤的MSD是由致残性职业慢性低 背痛（cLBP）。大多数背部损伤被认为是非特异性的，因为确切的伤害性原因通常 无法识别。在生物物理风险因素中，研究表明， 背部是下背痛的预测指标。举重是一项动态和高度可变的活动， 经常报告的导致腰痛的事件，占所有腰痛急症的近三分之一 房间参观以笨拙的姿势进行提升（不对称提升，较大的水平或垂直负载距离）， 更高的频率或进行多次重复可能会导致受伤的风险增加，由于增加的峰值 以及背部的累积机械负荷和疲劳效应。导致急性或 复发性背部损伤是过度用力，通过过度的峰值和累积的力量在背部， 参与急性下背部损伤进展为cLBP的因素包括不适应的运动控制策略， 肌肉过度活跃，减少运动变异性，以及恐惧认知的发展。该项目将 专注于开发具有集成生物反馈组件的机器人服装可以减少消耗， 鼓励安全、多样化的运动策略，促进康复。机器人服装将能够提供 当需要时，通过响应动态的自适应控制算法， 在不再需要援助时，将完全透明。这项技术也许能够 用于预防暴露于过度劳累的个体中的cLBP;通过这种方式，机器人服装将 补充在许多手工材料处理职业中实施的人机工程学培训，以鼓励 在工作场所采取适当的提升策略。机器人服装也将为物理治疗师提供一种新的工具， 临床社区通过协助恢复正常的安全进展来加强康复计划 活动，使人们能够更快地回到工作岗位，同时鼓励非适应不良的运动策略。 这项研究将采取分阶段的方法，并以人在环开发过程为基础， 通过频繁的人体研究评估组件和系统功能，其中定量（机器人， 生物力学和生理学）以及定性（即，人的因素）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.