HRV-Based Operator Fatigue Analysis and Classification Using Wearable Sensors

Al-Libawy, Hilal, Al-Ataby, Ali, Al-Nuaimy, Waleed ORCID: 0000-0001-8927-2368 and Al-Taee, Majid A ORCID: 0000-0002-3252-3637 (2016) HRV-Based Operator Fatigue Analysis and Classification Using Wearable Sensors. In: 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), 2016-3-21 - 2016-3-24.

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Abstract

Fatigue assessment and quantification are essential requirements to reduce the risks that occur as a consequence of a fatigued operator. The new wearable device technology offers an accurate measuring ability to one or more of fatigue-related biological data, which helps in quantifying fatigue levels in real-life environments. This paper presents a new heart rate variability (HRV) based operator-fatigue analysis and classification method using low-cost wearable devices. HRV that is considered a robust fatigue metric is measured by several wearable devices including a chest-strap heart monitor and a wrist watch that measures heart rate, skin temperature and skin conductivity. The data collected from real subjects are used to create a training dataset for fatigue analysis and classification. Two supervised machine-learning algorithms based on multi-layer neural network and support vector machine are developed and implemented to identify the alertness/fatigue states of the operator. Performance of the developed classifiers demonstrated high alertness/fatigue prediction accuracy. Such findings proved that the proposed analysis and classification method is valid and practically applicable.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	alertness, heart rate variability, neural networks, operator fatigue, support vector machine, wearable sensors
Depositing User:	Symplectic Admin
Date Deposited:	22 May 2017 09:35
Last Modified:	19 Jan 2023 07:04
DOI:	10.1109/ssd.2016.7473750
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3007443