A Computer Vision Lifting Monitor

计算机视觉升降监视器

• 批准号: 10693977
• 负责人: Jay M Kapellusch
• 金额: $ 51.81万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693977
• 关键词: Computer; Vision; Lifting; Monitor

Project Summary/ Abstract Repetitive manual lifting is a significant occupational health and safety concern and is highly prevalent in warehousing, distribution centers, package delivery, transportation, and lean manufacturing. These types of tasks are the most challenging to analyze from an ergonomics perspective, particularly in multi-task situations where lifting varied items occurs in numerous locations, involving variable body postures throughout the workday. Manually measuring the parameters needed for analysis is challenging and resource intensive for industry practitioners today. The overarching goal of this research is to create a computer vision risk model for lifting, incorporate it into a prototype instrument, and field evaluate the instrument in comparison to conventional RNLE methods. Automated job analysis potentially offers a more objective, accurate, repeatable, and efficient exposure assessment tool than conventional observational methods. Furthermore, it provides convenient quantification of additional exposure variables, including lifting kinematics (i.e., speed and acceleration) individual differences, and postures; is suitable for long-term, direct reading exposure assessment; and offers animated data visualization synchronized with video for identifying interventions. This research translates already collected videos of jobs and corresponding health outcomes from a landmark prospective study database for computer vision lower back pain risk assessment. It leverages the vast database of videos and corresponding exposure measures and health data for lifting and lowering activities (i.e., subtasks) performed by 772 workers across the three cohort studies, collected by our study partners at NIOSH, the University of Utah, and the University of Wisconsin-Milwaukee. They are part of a multi-institutional NIOSH funded consortium of U.S. laboratories that recently studied workers in a wide variety of industries in a prospective epidemiology study on lower back pain. The consortium videos will be analyzed by extracting the new video feature exposure measures, including lifting postures, and torso and load kinematics. The video exposure assessment data will be combined with consortium observational exposure measures and health outcome data. We will test the hypothesis that adding computer vision exposure variables with consortium exposure variables can enhance performance of predicting lower back pain. This project will refine and program video exposure assessment algorithms for posture classification, torso angle and trunk and load kinematics into a prototype device. The new exposure algorithms will be tested in selected industrial sites and compared against conventional observational methods for consistency and utility (r2p). This translational research offers an unprecedented opportunity to exploit unique videos and associated exposure and health outcome data already collected, in combination with new technology for quantifying exposures. This research addresses the manufacturing, and the transportation, warehousing, and utilities NORA sectors, as well as the musculoskeletal health cross sector agendas.

项目摘要/摘要 重复的手动解除是一个重大的职业健康和安全问题，并且非常普遍 仓储，配送中心，包装交付，运输和精益制造业。这些类型的 从人体工程学的角度来分析任务是最具挑战性的，尤其是在多项任务情况下 在许多位置发生了不同的项目，在整个工作日都涉及身体姿势。 手动衡量分析所需的参数对于行业来说是具有挑战性和资源密集的 今天的从业者。这项研究的总体目标是创建一个计算机视觉风险模型，以提升， 将其纳入原型仪器，并与常规RNLE相比评估该仪器 方法。自动化分析可能会提供更客观，准确，可重复和有效的暴露 评估工具比常规观察方法。此外，它提供了方便的量化 其他暴露变量，包括提升运动学（即速度和加速度）个体差异， 和姿势；适用于长期直接阅读暴露评估；并提供动画数据 可视化与视频同步以识别干预措施。这项研究翻译已经收集了 来自具有里程碑意义的前瞻性研究数据库的工作和相应健康结果的视频 视力下背部疼痛风险评估。它利用了庞大的视频数据库和相应的曝光 772名工人在整个措施和降低活动的措施和健康数据（即子任务）中 我们的研究伙伴在NIOSH，犹他大学和大学收集的三项研究研究 威斯康星州米尔沃基。它们是由NIOSH资助的美国实验室财团的一部分 最近在一项有关下背部疼痛的前瞻性流行病学研究中研究了各种行业的工人。 联盟视频将通过提取新视频功能曝光措施来分析，包括提升 姿势，躯干和负载运动学。视频曝光评估数据将与财团合并 观察暴露措施和健康结果数据。我们将测试添加计算机的假设 与财团暴露变量的视力暴露变量可以提高预测下背部的性能 疼痛。该项目将完善和程序视频曝光评估算法，用于姿势分类，躯干 角度和躯干，并将运动学加载到原型设备中。新的曝光算法将在 选定的工业站点，并与常规观察方法进行比较以保持一致性和实用性 （R2P）。这项翻译研究提供了一个前所未有的机会，可以利用独特的视频和相关的视频 接触和健康结果数据已经收集了，结合了新技术来量化 暴露。这项研究涉及制造业，运输，仓储和公用事业 部门以及肌肉骨骼健康跨部门议程。