Yen Thomas Y, Radwin Robert G
Department of Industrial Engineering, University of Wisconsin-Madison, 53705, USA.
Appl Ergon. 2002 Jan;33(1):85-93. doi: 10.1016/s0003-6870(01)00029-1.
This study compares the time needed to analyze data and the inter-analyst variability using observational posture classification vs. spectral analysis of upper limb kinematic measurements made using an electrogoniometer for selected industrial jobs. Eight trained analysts studied four jobs using both methods. An incomplete fixed block experimental design was used, whereby each analyst used one method for each job. The four jobs included (1) punch press operation, (2) packaging, (3) parts hanging, and (4) construction vehicle operation. The posture classification analysis method involved visually classifying tipper extremity joint angles into specific zones relative to the range of motion for every one-third second (10 frames) of videotape. Spectral analysis required the analysts to identify cycle break points. The electrogoniometer signals were synchronized with each cycle, and power spectra for each joint were computed. The average difference in RMS joint deviation among analysts was 0.9 (SD = 0.61 degrees) for spectral analysis and 7.1 (SD = 2.53 degrees) for posture classification. The average difference in mean joint angle was 0.8 (SD = 0.59 degrees) for spectral analysis and 11.4 (SD = 1.58 degrees) for posture classification. Repetition frequency differed an average of 0.05 Hz (SD = 0.054 Hz) for spectral analysis and 0.07 Hz (SD = 0.058 Hz) for posture classification. Posture classification took a factor of 6.3 more time than cycle break point assignment for spectral analysis. Even considering the additional time needed for sensor attachment for direct measurement, posture classification took an average factor of 1.29 more time than spectral analysis using electrogoniometer data.
本研究比较了使用观察性姿势分类法与使用电子角度计对选定工业工作的上肢运动测量进行频谱分析时，分析数据所需的时间以及分析人员之间的变异性。八位经过培训的分析人员使用这两种方法研究了四项工作。采用了不完全固定区组实验设计，即每位分析人员对每项工作使用一种方法。这四项工作包括：（1）冲压机操作，（2）包装，（3）零件悬挂，以及（4）工程车辆操作。姿势分类分析方法涉及将录像带每三分之一秒（10帧）的上肢关节角度相对于运动范围直观地分类到特定区域。频谱分析要求分析人员识别周期断点。将电子角度计信号与每个周期同步，并计算每个关节的功率谱。对于频谱分析，分析人员之间RMS关节偏差的平均差异为0.9（标准差=0.61度），对于姿势分类为7.1（标准差=2.53度）。对于频谱分析，平均关节角度的平均差异为0.8（标准差=0.59度），对于姿势分类为11.4（标准差=1.58度）。频谱分析的重复频率平均相差0.05Hz（标准差=0.054Hz），姿势分类为0.07Hz（标准差=0.058Hz）。姿势分类比频谱分析的周期断点赋值多花费6.3倍的时间。即使考虑到直接测量时传感器附着所需的额外时间，姿势分类比使用电子角度计数据的频谱分析平均多花费1.29倍的时间。
