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

• 成果类型: Article
• 署名作者: Park, Chiwoo; Do Noh, Sang; Srivastava, Anuj
• 署名单位: State University System of Florida; Florida State University; Sungkyunkwan University (SKKU); State University System of Florida; Florida State University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2021.2216
• 发表日期: 2022
• 页码: 3217-3233
• 关键词: motion and time study; motion sensors; Riemannian manifold; probability over a manifold; motion space; rate space

摘要

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.