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

Chiwoo Park (), Sang Do Noh () and Anuj Srivastava ()
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Chiwoo Park: Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, Florida 32306
Sang Do Noh: Department of Systems Management Engineering, Sungkyunkwan University, Suwon, South Korea
Anuj Srivastava: Department of Statistics, Florida State University, Tallahassee, Florida 32306

Operations Research, 2022, vol. 70, issue 6, 3217-3233

Abstract: 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.

Keywords: Machine Learning and Data Science; motion and time study; motion sensors; Riemannian manifold; probability over a manifold; motion space; rate space (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:70:y:2022:i:6:p:3217-3233

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