Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can...

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Bibliographic Details
Main Authors: Lingjian Kong, Kai Xie, Kaixuan Niu, Jianbiao He, Wei Zhang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Sensors
Subjects:
intelligent traffic
fatigue detection
multi-modal feature fusion
heart rate
bidirectional LSTM
Online Access:https://www.mdpi.com/1424-8220/24/2/455

Internet

https://www.mdpi.com/1424-8220/24/2/455

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