Akkas Oguz, Lee Cheng Hsien, Hu Yu Hen, Harris Adamson Carisa, Rempel David, Radwin Robert G
a Department of Industrial and Systems Engineering , University of Wisconsin-Madison , Madison , WI , USA.
b Department of Electrical and Computer Engineering , University of Wisconsin-Madison , Madison , WI , USA.
Ergonomics. 2017 Dec;60(12):1730-1738. doi: 10.1080/00140139.2017.1346208. Epub 2017 Jul 6.
Two computer vision algorithms were developed to automatically estimate exertion time, duty cycle (DC) and hand activity level (HAL) from videos of workers performing 50 industrial tasks. The average DC difference between manual frame-by-frame analysis and the computer vision DC was -5.8% for the Decision Tree (DT) algorithm, and 1.4% for the Feature Vector Training (FVT) algorithm. The average HAL difference was 0.5 for the DT algorithm and 0.3 for the FVT algorithm. A sensitivity analysis, conducted to examine the influence that deviations in DC have on HAL, found it remained unaffected when DC error was less than 5%. Thus, a DC error less than 10% will impact HAL less than 0.5 HAL, which is negligible. Automatic computer vision HAL estimates were therefore comparable to manual frame-by-frame estimates. Practitioner Summary: Computer vision was used to automatically estimate exertion time, duty cycle and hand activity level from videos of workers performing industrial tasks.
开发了两种计算机视觉算法，用于从执行50项工业任务的工人视频中自动估计工作时间、占空比（DC）和手部活动水平（HAL）。对于决策树（DT）算法，手动逐帧分析与计算机视觉DC之间的平均DC差异为-5.8%，而特征向量训练（FVT）算法为1.4%。DT算法的平均HAL差异为0.5，FVT算法为0.3。进行了一项敏感性分析，以检查DC偏差对HAL的影响，结果发现当DC误差小于5%时，HAL不受影响。因此，DC误差小于10%对HAL的影响将小于0.5 HAL，这可以忽略不计。因此，计算机视觉自动HAL估计与手动逐帧估计相当。从业者总结：计算机视觉用于从执行工业任务的工人视频中自动估计工作时间、占空比和手部活动水平。
