Fatigue monitoring using wearables and AI: Trends, challenges, and future opportunities

Kourosh Kakhi · Senthil Kumar Jagatheesaperumal · Abbas Khosravi · Roohallah Alizadehsani · U. Rajendra Acharya

74 citations0/0 files storedComputers in Biology and Medicine

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Sleep and Work-Related Fatigue Context-Aware Activity Recognition Systems Non-Invasive Vital Sign Monitoring

Abstract

Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand and high-risk environments such as transportation, construction, healthcare, and manufacturing. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person's level of exhaustion. This has allowed for timely insights into preventing hazards associated with fatigue. This review focuses on wearable technology and artificial intelligence (AI) integration for fatigue detection, using a systematic approach guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Studies that used signal processing methods to extract pertinent aspects from physiological data, such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG), among others, were analyzed as part of the systematic review process. These features were then analyzed using models, to detect patterns of fatigue and anticipate its onset. By merging data from several sources, information fusion techniques enhanced the precision and dependability of fatigue evaluation. The findings demonstrate that wearable devices can accurately identify fatigue through multi-modal signal analysis when combined with advanced ML/DL models. Information fusion techniques, which combine data from multiple sensors, significantly improved the accuracy and reliability of fatigue assessments. These developments open the door for more improvements in this field and offer valuable tools for enhancing safety and reducing fatigue-related hazards.

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2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices 2010Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm 2017Monitoring fatigue in construction workers using physiological measurements 2014YawDD 2016Drowsiness detection using heart rate variability 2015Fatigue management in the workplace 2021Fatigue Monitoring Through Wearables: A State-of-the-Art Review

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