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

项目摘要/摘要 这项研究是NIH帮助结束成瘾长期(Hear)倡议的一部分，该倡议旨在加快科学解决方案，以了解疼痛的基础并加强临床疼痛管理。 对于在建筑、制造、物流、医疗保健和农业等行业工作的许多人来说，典型的工作日包括频繁的过度运动(重复的弯曲、扭转、抬起和持续的屈曲姿势)，这会增加背部受伤的风险。过度劳累是导致肌肉骨骼疾病(MSD)的最常见事件-超过65%的MSD与过度劳累有关，而导致休班的所有MSD中有三分之一是由致残的职业性慢性下腰痛(CLBP)造成的。大多数背部损伤被认为是非特异性的，因为确切的伤害原因通常是不可能识别的。在生物物理风险因素中，研究表明，背部累积和峰值负荷是下腰痛的预测因素。提举是一种动态和高度可变的活动，也是最常见的导致腰痛的事件，占所有背痛急诊室就诊的近三分之一。以笨拙的姿势(不对称的抬起、较大的水平或垂直载荷距离)、高频或多次重复举重可能会增加受伤风险，因为背部的峰值和累积机械负荷增加以及疲劳效应。导致急性或急性死亡的主要因素 复发性背部损伤是由于背部过度的峰值和累积力造成的过度劳累，急性下背部损伤进展为cLBP的主要因素包括适应不良的运动控制策略、肌肉多动、运动变异性降低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，可以减少体力消耗，鼓励安全、多样化的运动策略，并促进恢复。机器人服装将能够在需要时通过对动态运动做出反应的自适应控制算法为背部和髋关节提供支撑力，并在不再需要帮助时变得完全透明。这项技术可能能够用于预防过度劳累的个人发生cLBP；通过这种方式，机器人服装将补充许多手工材料处理职业中实施的人体工学培训，以鼓励工作场所正确的举起策略。机器人服装还将为理疗师和 临床社区通过帮助人们安全地恢复正常活动来加强康复计划，使人们能够更早地恢复工作，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，并以人在环中的开发过程为基础，通过频繁的人类受试者研究来评估部件和系统功能，其中将收集定量(机器人、生物力学和生理学)以及定性(即人为因素)数据。

PROJECT SUMMARY/ABSTRACT This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to understand the basis of pain and enhance clinical pain management. 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.