Video Motion Analysis of Repetitive Exertions

重复运动的视频运动分析

• 批准号: 8384915
• 负责人: ROBERT G RADWIN
• 金额: $ 21.77万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8384915
• 关键词: Acceleration; Accounting; Algorithms; Centers for Disease Control and Prevention (U.S.); Correlation Studies; Data; Data Collection; Data Set; Databases; Dose; Exertion; Freedom; Frequencies; Goals; Hand; Health; Human; Image; Industry; Injury; Laboratories; Lead; Location; Logistic Regressions; Manuals; Measurement; Measures; Methodology; Methods; Modeling; Motion; Musculoskeletal Diseases; National Institute for Occupational Safety and Health; Occupational; Outcome; Participant; Prevention; Primary Prevention; Probability; Process; Reading; Recovery; Recruitment Activity; Reporting; Research; Risk; Safety; Stress; System; Technology; Time; Training; Translating; Upper Extremity; Work; Workers' Compensation; Workplace; arm; base; cost; design; digital; epidemiology study; health practice; indexing; instrument; interest; kinematics; musculoskeletal injury; novel; operation; prospective; response; sensor; validation studies

DESCRIPTION (provided by applicant): Upper extremity musculoskeletal injuries are common in hand intensive work involving highly repetitive motions and exertions. Previous exposure assessment methods involve either direct measurements using instruments attached to a worker's hands or arms, or indirect observations. Both methods however are highly impractical for occupational health practice. Compared to instruments, indirect observation lacks precision and accuracy, is not suitable for long observation periods, and requires considerable analyst time. Alternatively, attaching sensors on working hands is too time consuming, and sensors may interfere with normal working operation. We are developing novel real- time video processing algorithms to automatically and unobtrusively measure upper limb kinematics from conventional single camera digital video. In this exploratory research, we propose using a video feature extraction method to indirectly quantify hand activity using markerless video motion analysis. We are collaborating with the NIOSH Industry-wide Studies Branch, which is making more than 700 videos of 484 workers at three study locations, their associated observational analyses and longitudinal health outcome data available to us for this initial study. The real time video algorithms will first be developed and designed to be robust using sequential Bayesian estimation to track the motion of a general region of interest (ROI) on the upper extremities selected by the camera operator or analyst, and statistically estimating its velocity and acceleration. Video derived ROI kinematics from varying vantage points will then be compared against ground truth measurements obtained using a 3D infrared motion capture system, and conventional observational measurements of laboratory participants performing controlled repetitive motion tasks. We will also perform secondary analysis of videos from actual workplace activities taken and already studied by NIOSH, for algorithm refinement and for studying the correlation between the kinematic data and conventional observational exposure measures. We will evaluate the risk of an injury from the NIOSH health outcome data using our new exposure analysis method and compare the resulting dose-response model using the measured frequency, duty cycle, velocity and acceleration from our automatic video analysis method against conventional manual analysis in order to evaluate improvement over existing methods. This exploratory research could ultimately lead to a full-scale epidemiology and validation study of our video exposure assessment methodology that would leverage the existing NIOSH consortium database from seven participating laboratories in order to establish a model relating video derived kinematics and health outcome. The long-term goal is to develop a video-based direct reading exposure assessment instrument for upper limb repetitive motion activities that should be useful for evaluating the risk of injuries in the workplace, and for primry prevention. PUBLIC HEALTH RELEVANCE: Upper extremity musculoskeletal injuries are common in hand intensive work involving highly repetitive motions and exertions. The long--‐term goal of this research is to develop a video--‐based direct reading exposure assessment instrument for upper limb repetitive motion activities that should be useful for evaluating the ris of injuries in the workplace, and for primary prevention.

描述(由申请人提供)：上肢肌肉骨骼损伤在手部高强度工作中很常见，涉及高度重复的动作和劳作。以前的暴露评估方法要么是使用安装在工人手上或手臂上的仪器进行直接测量，要么是间接观察。然而，这两种方法在职业健康实践中都是非常不切实际的。与仪器相比，间接观测缺乏精密度和准确度，不适合较长的观测周期，需要相当长的分析时间。或者，在工作手上安装传感器太耗时，传感器可能会干扰正常的工作操作。我们正在开发新的实时视频处理算法，以便从传统的单摄像机数字视频中自动且不引人注目地测量上肢运动学。在这项探索性研究中，我们提出了一种利用无标记视频运动分析来间接量化手部活动的视频特征提取方法。我们正在与NIOSH全行业研究分部合作，该分部正在制作三个研究地点的484名工人的700多个视频，他们相关的观察分析和纵向健康结果数据，供我们用于这项初步研究。《实时》 视频算法将首先使用顺序贝叶斯估计来跟踪摄像机操作员或分析师选择的上肢上的一般感兴趣区域(ROI)的运动，并从统计上估计其速度和加速度，从而开发和设计出稳健的视频算法。然后，从不同的有利位置获得的视频ROI运动学将与使用3D红外运动捕获系统获得的地面真实测量结果以及执行受控重复运动任务的实验室参与者的传统观测测量结果进行比较。我们还将对NIOSH拍摄和研究的实际工作场所活动的视频进行二次分析，以改进算法，并研究运动学数据与传统观测暴露措施之间的相关性。我们将使用我们的新暴露分析方法从NIOSH健康结果数据评估受伤风险，并使用我们的自动视频分析方法测得的频率、占空比、速度和加速度与传统的手动分析方法比较所产生的剂量-反应模型，以评估对现有方法的改进。这项探索性研究最终可能导致对我们的视频暴露评估方法进行全面的流行病学和验证性研究，该研究将利用来自七个参与实验室的现有NIOSH联盟数据库，以建立一个将视频衍生运动学和健康结果联系起来的模型。长期目标是开发一种用于上肢重复运动活动的基于视频的直读暴露评估工具，该工具应有助于评估工作场所的伤害风险，并用于初级预防。 公共卫生相关性：上肢肌肉骨骼损伤在涉及高度重复动作和用力的手部高强度工作中很常见。这项研究的长期目标是开发一种基于视频的上肢重复运动活动的直读暴露评估工具，该工具应有助于评估工作场所伤害的RIS，并用于一级预防。