Motion capture for operations analysis: a footwear sector case study
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Abstract
The footwear sector in Colombia is predominantly composed of micro, small, and medium-sized enterprises (MSMEs) that rely heavily on manual processes and low technological adoption. This study aims to validate a
methodology for motion-based operation analysis in real production environments, using optical motion capture (MoCap) systems integrated with virtual simulations. A footwear company’s manual assembly was analyzed
using four infrared cameras and reflective markers placed on workers’ wrists and index fingers. The motion data were processed in MATLAB to recreate the operation virtually and identify types of movements and frequency . Results showed a total of 539 movements during a 90-second
cycle, 215 operations (39.9%), 35 transports (6.5%), and 289 hold positions (53.6%). The average time per productive operation was 0.41 seconds, yielding an estimated productivity of 146 operations per minute. The virtual
simulation highlighted a concentration of hand trajectories within the normal reach zone, indicating an efficient spatial arrangement. However, a high proportion of nonproductive movements revealed significant opportunities for method standardization and ergonomic design improvement. MoCap system implementation of enabled automated movement classification and trajectory analysis without manual segmentation, thus overcoming the subjectivity and limitations of traditional observation. Despite minor data losses due to environmental interferences, the system proved robust and applicable in an active industrial setting. This research demonstrates the feasibility of incorporating MoCap technology into time and motion studies for real manufacturing contexts, offering helpful tips for process
redesign, operator training, and ergonomic assessment.
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