Physiological data measurement in digital manufacturing

Agrawal, Subham; Chong, Junjie; Yacoub, Ali A.; Giuliani, Manuel; Jafari, Aghil; Etoundi, Appolinaire

Abstract

As industry is moving towards a new digital rev-olution, identifying workers' mental and physical status is key to improved productivity in a digital manufacturing scenario. The main objective here is to provide an overview of sensing technologies in digital manufacturing and discuss suitability for taking physiological measurements of workers collaborating with robots. A method for rating physiological sensors in digital manufacturing application areas has been discussed which takes into account expert reviews. Selected commercially-available sensors are rated based on 9 evaluation keys (wearability, form-factor, mobility, pre-training, data-exchange capability, on-board filtering, ease-of-use, cost, and calibration) for digital manufacturing. The result is a scorecard of available sensors with feasibility to be used in digital manufacturing. In a given category, this data allows the selection of the best available sensors for certain use cases. The method to score the sensors has been explicitly explained to allow readers to expand on and contribute towards the data.

Citation

Agrawal, S., Chong, J., Yacoub, A. A., Giuliani, M., Jafari, A., & Etoundi, A. (2022). Physiological data measurement in digital manufacturing. In 2021 24th International Conference on Mechatronics Technology (ICMT). https://doi.org/10.1109/ICMT53429.2021.9687200

• Conference Name: International Conference on Mechatronics Technology ICMT 2021
• Conference Location: Singapore
• Start Date: Dec 18, 2021
• End Date: Dec 22, 2021
• Acceptance Date: Nov 7, 2021
• Online Publication Date: Feb 1, 2022
• Publication Date: Feb 1, 2022
• Deposit Date: Jan 28, 2022
• Publicly Available Date: Feb 2, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2021 24th International Conference on Mechatronics Technology (ICMT)
• ISBN: 9781665424592
• DOI: https://doi.org/10.1109/ICMT53429.2021.9687200
• Public URL: https://uwe-repository.worktribe.com/output/8690512