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

TL;DR

This paper introduces a new mathematical framework for analyzing work motions and execution rates using modern motion sensor data, addressing limitations of manual analysis and enabling automated, data-driven insights in manufacturing.

Contribution

It develops a novel mathematical and statistical framework for motion and time analysis from sensor data, advancing beyond manual methods.

Findings

Framework successfully applied to manufacturing data

Identifies correlations between motions and work rates

Enhances automated analysis of motion sensor data

Abstract

The motion-and-time analysis has been a popular research topic in operations research, especially for analyzing work performances in manufacturing and service operations. It is regaining attention as continuous improvement tools for 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. The past analyses largely relied on manual steps involving time-consuming stop-watching and video-taping, followed by manual data analysis. While modern sensing devices have automated the collection of motion data, the motion analytics that transform the new data into knowledge are largely underdeveloped. Unsolved technical questions include: How the motion and time information can be extracted from the motion sensor data, how work motions and execution rates are statistically modeled and compared, and what are the statistical correlations of motions to the rates? In this paper, we develop a novel mathematical framework for motion and time analysis with motion sensor data, by defining new mathematical representation spaces of human motions and execution rates and by developing statistical tools on these new spaces. This methodological research is demonstrated using five use cases applied to manufacturing motion data.