Video Motion Analysis of Repetitive Exertions

描述（由申请人提供）：上肢肌肉骨骼损伤是常见的手密集型工作，涉及高度重复的运动和努力。以前的接触评估方法要么是直接测量，使用仪器连接到工人的手或手臂，或间接观察。然而，这两种方法对于职业健康实践来说都是非常不切实际的。与仪器相比，间接观察缺乏精密度和准确度，不适合长时间观察，需要大量的分析时间。可替代地，将传感器附接在工作手上太耗时，并且传感器可能干扰正常的工作操作。我们正在开发新的真实的实时视频处理算法，以自动和不显眼地测量上肢运动学从传统的单摄像机数字视频。在这项探索性的研究中，我们提出了使用视频特征提取方法间接量化的手活动，使用无标记的视频运动分析。我们正在与NIOSH全行业研究分支合作，该分支正在制作三个研究地点的484名工人的700多个视频，他们的相关观察分析和纵向健康结果数据可供我们用于这项初步研究。真实的时间 视频算法将首先被开发和设计为鲁棒的，其使用顺序贝叶斯估计来跟踪由摄像机操作员或分析员选择的上肢上的一般感兴趣区域（ROI）的运动，并统计地估计其速度和加速度。然后将来自不同Vantage位置的视频导出的ROI运动学与使用3D红外运动捕获系统获得的地面实况测量值以及执行受控重复运动任务的实验室参与者的常规观察测量值进行比较。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。我们将使用我们新的暴露分析方法评估NIOSH健康结果数据中的损伤风险，并将使用我们的自动视频分析方法测量的频率，占空比，速度和加速度与传统的手动分析方法进行的剂量反应模型进行比较，以评估对现有方法的改进。这项探索性研究最终可能导致我们的视频暴露评估方法的全面流行病学和验证研究，该方法将利用来自七个参与实验室的现有NIOSH联盟数据库，以建立一个与视频衍生运动学和健康结果相关的模型。长期目标是开发一种基于视频的直接阅读暴露评估工具，用于上肢重复性运动活动，这对评估工作场所的伤害风险和初级预防是有用的。

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.