Abstract
Spectral analysis of upper limb kinematic measurements has been previously demonstrated useful for quantifying physical stress properties of repetitive motion. The method requires manually separating the data into segments corresponding to individual tasks or work elements and computing power spectra. This study investigated using signal pattern recognition to help automate the analysis by separating the data through identification of stereotypical patterns in cyclical tasks. Joint angular data was collected for five industrial jobs using electrogoniometers attached to the wrist, elbow and shoulder of the dominant limb. A multimedia computer system along with the analyst interactively indicate element break points. The break points were also automatically identified using a template matching (TM) algorithm. The algorithm identified the cycle break points on average to within 0.997 s (S.D.=2.762 s) of the human analyst's reference break point. Nevertheless, automated break point identification should be useful for indicating approximate break point and then interactively fine-tuning the computer as a means for reducing the time required to perform an analysis.
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