Data Science for Motion and Time Analysis with Modern Motion Sensor Data

The analysis of motion and time has become significant in operations research, especially for analyzing work performance in manufacturing and service operations in the development of lean manufacturing and smart factory. This paper develops a framework for data-driven analysis of work motions and studies their correlations to work speeds or execution rates, using data collected from modern motion sensors. Past efforts primarily relied on manual steps involving time-consuming stop-watching, videotaping, and manual data analysis. Whereas modern sensing devices have automated motion data collection, the motion analytics that transform the new data into knowledge are largely underdeveloped. Unsolved technical questions include: How can the motion and time information be extracted from the motion sensor data? How are work motions and execution rates statistically modeled and compared? How are the motions correlated to the rates? This paper develops a novel mathematical framework for motion and time analysis using motion sensor data by defining new mathematical representation spaces of human motions and execution rates and developing statistical tools on these new spaces. The paper demonstrates this comprehensive methodology using five use cases applied to manufacturing motion data.

Funding: Support from the Brain Pool Fellowship of the National Research Foundation Korea [Grant 2019H1D3A2A01100649] is gratefully acknowledged.

Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.2216.