Journal
ERGONOMICS
Volume 63, Issue 7, Pages 831-849Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00140139.2020.1759700
Keywords
Performance modelling; wearable technologies; repetitive precision task; classification
Funding
- Chinese Scholarship Council
- National Natural Science Foundation of China [71471095, 71942005]
- Ministry of Science and Technology of the People's Republic of China [2016IM010200, 2017YFC0820200, 2015IM040100]
- Wisdom Gathered in Zhengzhou 1125 Talents Recruitment Programme [[2017]121]
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In modern manufacturing systems, especially assembly lines, human input is a critical resource to provide dexterity and flexibility. However, the repetitive precision tasks common in assembly lines can have adverse effects on workers and overall system performance. We present a data-driven approach to evaluating task performance using wearable sensor data (kinematics, electromyography and heart rate). Eighteen participants (gender-balanced) completed repeated cycles of maze tracking and assembly/disassembly. Various combinations of input data types and classification algorithms were used to model task performance. The use of the linear discriminant analysis (LDA) algorithm and kinematic data provided the most promising classification performance. The highest model accuracy was found using the LDA algorithm and all data types, with respective levels of 62.4, 88.6, 85.8 and 94.1% for predicting maze errors, maze speed, assembly/disassembly errors and assembly/disassembly speed. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly-lines or similar industries. Practitioner summary: This paper proposed models the repetitive precision task performance using data collected from wearable sensors. The use of the LDA algorithm and kinematic data provided the most promising classification performance. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly lines or similar industries.
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