Individualisation method of biomathematical model of fatigue for predicting individual performance in mild and irregular sleep deprivation

Abstract

Individual biomathematical models of fatigue (BMMF) are promising tools for detecting fatigue and possible incidents. Existing individual BMMFs have been validated in laboratory experiments in which subjects experience total sleep deprivation (TSD) and regular chronic sleep deprivation (CSD). However, some shift populations experience mild sleep deprivation (MSD) or irregular sleep deprivation (ISD) in real life. We employed the adaptive momentum estimation algorithm to adjust the classical SAFTE model for an individual. Model individualisation can be performed in real-time when new performance data are collected. The individual SAFTE model was compared with existing BMMFs in TSD, CSD, MSD, and ISD. The validation results show that the individual SAFTE model has advantages in MSD and ISD datasets collected from officers and truck drivers in real life. This study expands previous research results on the real-time individualisation of BMMFs and exposes individual BMMFs to various sleep-deprivation conditions in the field.

Practitioner summary: This study proposes an individual biomathematical models of fatigue to predict human performance in mild and irregular sleep deprivation. The validation results in both laboratory and field show the proposed model has advantages over existing models when predicting officers’ and truck drivers’ performance in real life.

Keywords:

• Biomathematical model of fatigue
• individual model
• mild sleep deprivation
• irregular sleep deprivation
• validation in the field

Author contributions

Jiawei Fu: model establishment, algorithm design, collection and analysis of data, drafting the article and revising critically. Liang Ma: conceptualization, writing-reviewing, general supervision, funding acquisition, final approval of the version to be published.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

TSD and CSD datasets in Section 2.3.1 are available in References (Ramakrishnan et al. 2015; Liu et al. 2017). MSD and ISD datasets in Section 2.3.1 are available in GitHub (https://github.com/JiaweiFu65/IndividualSAFTE).

Additional information

Funding

This document is the result of the research project funded by the National Natural Science Foundation of China [grant numbers 71942005 and 72171127] and the Research Center of Intelligent Logistics and Supply Chain System of Tsinghua University [grant number THUCSL20182911756-011].