A comparison between analysis time and inter-analyst reliability using spectral analysis of kinematic data and posture classification.

ABSTRACT

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.