A study to trial the use of inertial non-optical motion capture for ergonomic analysis of manufacturing work. (January 2018)

• Record Type: Journal Article
• Authors: Fletcher, Sarah R; Johnson, Teegan L; Thrower, John
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 1(2018)
• Page Start: 90
• Page End: 98
• Publication Date: 2018-01
• Subjects: Motion capture -- human data analysis -- manufacturing system design -- digital human modelling; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405416660997
• Languages: English
• ISSNs: 0954-4054

Abstract

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors.