p Data Science for Motion and Time Analysis with Modern Motion Sensor Data
- Authors
- Park, C[Park, Chiwoo]; Noh, SD[Noh, Sang Do]; Srivastava, A[Srivastava, Anuj]
- Issue Date
- 1-Nov-2022
- Publisher
- INFORMS
- Keywords
- motion and time study; motion sensors; Riemannian manifold; probability over a manifold; motion space; rate space
- Citation
- OPERATIONS RESEARCH, v.70, no.6, pp.3217 - 3233
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- OPERATIONS RESEARCH
- Volume
- 70
- Number
- 6
- Start Page
- 3217
- End Page
- 3233
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/95326
- DOI
- 10.1287/opre.2021.2216
- ISSN
- 0030-364X
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Engineering > Department of Systems Management Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.