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Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

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Related Experiment Video

Updated: Jun 28, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Explainable Liquid Time-Constant Network for Multi-Modal Fatigue Detection in Healthcare 4.0.

Xu Xu, Ghulam Muhammad

IEEE Journal of Biomedical and Health Informatics

|May 5, 2026

Summary

This summary is machine-generated.

This study introduces LTC-DFD, an explainable AI framework for driver fatigue detection. It effectively models time-varying dynamics and fuses multi-modal signals, enhancing road safety in healthcare 4.0 systems.

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Last Updated: Jun 28, 2026

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Area of Science:

Artificial Intelligence
Biomedical Engineering
Transportation Safety

Background:

Driver fatigue is a critical safety concern, especially within healthcare 4.0 systems.
Existing fatigue detection methods struggle with time-varying dynamics and multi-modal signal fusion.
Dependencies within and between physiological signals are often overlooked.

Purpose of the Study:

To propose an explainable AI (XAI) framework, LTC-DFD, for advanced multi-modal driver fatigue detection.
To address limitations in modeling temporal dynamics and fusing multi-modal data.
To enhance the reliability and trustworthiness of driver monitoring systems.

Main Methods:

Developed a novel framework (LTC-DFD) with five parallel branches for distinct physiological modalities.
Incorporated Liquid Time-Constant (LTC) blocks to model temporal dynamics via trainable differential equations.
Implemented a dual-level attention mechanism (channel and token-level) for intra- and inter-modal feature fusion.

Main Results:

Achieved high accuracy (96.5%) and low RMSE (0.22) on the SEED-VIG dataset.
Demonstrated superior performance compared to state-of-the-art methods with a low parameter count (0.42 M).
Learned temporal dynamics and attention patterns align with neurophysiological markers of drowsiness.

Conclusions:

LTC-DFD offers a robust and explainable solution for multi-modal driver fatigue detection.
The framework's ability to model complex dynamics and fuse signals enhances its applicability in healthcare 4.0.
Results support the trustworthy deployment of LTC-DFD for real-time driver monitoring and road safety.