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.

项目总结/摘要 重复的手动提升是一个重要的职业健康和安全问题， 仓储、配送中心、包裹递送、运输和精益制造。这些类型的 从人机工程学的角度来看，任务是最具挑战性的分析，特别是在多任务的情况下 其中，在许多位置发生提升不同的物品，在整个工作日中涉及可变的身体姿势。 手动测量分析所需的参数对于工业来说是具有挑战性的并且是资源密集型的 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， 将其纳入原型仪器，并与传统RNLE进行比较，对仪器进行现场评估 方法.自动化作业分析可能提供更客观、准确、可重复和有效的曝光 与传统的观察方法相比，此外，它还提供了方便的量化方法， 另外的暴露变量，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 与视频同步的可视化，用于识别干预措施。本研究翻译已收集 具有里程碑意义的前瞻性计算机研究数据库中的工作和相应健康结果视频 视力下背痛风险评估。它利用了庞大的视频数据库和相应的曝光率 用于提升和降低活动的测量和健康数据（即，由772名工人在整个 三项队列研究，由我们在NIOSH、犹他州大学和犹他州大学的研究伙伴收集。 威斯康星州密尔沃基。他们是NIOSH资助的多机构美国实验室联盟的一部分， 最近在一项关于下背痛的前瞻性流行病学研究中，研究了各种行业的工人。 该联盟的视频将通过提取新的视频特征曝光措施进行分析，包括解除 姿势、躯干和负载运动学。视频曝光评估数据将与联合体 观察性暴露措施和健康结果数据。我们将检验一个假设， 视觉暴露变量与联合体暴露变量可以提高预测下背部的性能 痛苦这个项目将完善和程序视频曝光评估算法的姿势分类，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 选定的工业地点，并与传统的观测方法进行比较，以确保一致性和实用性 （r2p）。这种转化研究提供了前所未有的机会，利用独特的视频和相关的 已经收集的暴露和健康结果数据，结合新的量化技术， 暴露。本研究针对制造业、运输业、仓储业、公用事业诺拉业 各部门以及肌肉骨骼健康跨部门议程。