Wearable network for multilevel physical fatigue prediction in manufacturing workers

Payal Mohapatra, Vasudev Aravind, Marisa Bisram, Young Joong Lee, Hyoyoung Jeong, Katherine Jinkins, Richard Gardner, Jill Streamer, Brent Bowers, Lora Cavuoto, Anthony Banks, Shuai Xu, John Rogers, Jian Cao, Qi Zhu*, Ping Guo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject’s physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system’s practical applicability and contributes a valuable open-access database for future research.

Original languageEnglish (US)
Article numberpgae421
JournalPNAS Nexus
Volume3
Issue number10
DOIs
StatePublished - Oct 1 2024

Funding

This research was supported by MxD (Manufacturing \u00D7 Digital Institute) under project numbers 19-13-05 and 22-06-02. This project was completed under the Technology Investment Agreement W15QKN-19-3-0003, between Army Contracting Command\u2014New Jersey and MxD. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Army.

Keywords

  • continuous fatigue monitoring
  • manufacturing
  • quantifying physical fatigue
  • real-time machine learning
  • wearable sensors

ASJC Scopus subject areas

  • General

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