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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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CogMamba: Multi-Task Driver Cognitive Load and Physiological Non-Contact Estimation with Multimodal Facial Features.

Yicheng Xie1, Bin Guo1

  • 1School of Electrical Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|September 27, 2025
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Summary
This summary is machine-generated.

This study introduces CogMamba, a non-contact model for estimating driver cognitive load and physiological state using facial video. It offers a practical solution for monitoring drivers in autonomous vehicles without invasive equipment.

Keywords:
Mambacognitive load detectionmulti-task learningnon-contact monitoringphysiological measurement

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Area of Science:

  • Human-Computer Interaction
  • Automotive Safety
  • Biomedical Engineering

Background:

  • Driver cognitive load is critical for advanced driving assistant systems (ADAS) and autonomous driving safety.
  • Current methods for detecting driver cognitive load are often invasive or limited, hindering practical application.
  • Non-driving-related tasks (NDRTs) can increase cognitive load, requiring rapid driver re-engagement.

Purpose of the Study:

  • To develop a non-contact, multi-task model for estimating driver cognitive load and physiological state.
  • To address the limitations of existing invasive or eye-tracking-based cognitive load detection methods.
  • To enhance the safety and practicality of ADAS and autonomous driving systems.

Main Methods:

  • Proposed a novel non-contact cognitive load and physiological state estimation model named CogMamba.
  • Utilized multimodal features extracted from RGB facial videos.
  • Introduced the Mamba architecture to capture temporal dependencies for joint estimation of cognitive load, heart rate (HR), and respiratory rate (RR).

Main Results:

  • CogMamba demonstrated superior performance on two public datasets.
  • The model showed excellent robustness in cross-dataset generalization tests.
  • Successfully achieved joint estimation of cognitive load, HR, and RR using non-contact video data.

Conclusions:

  • CogMamba offers an efficient and practical solution for non-contact driver state monitoring.
  • The findings provide valuable insights for real-world applications in autonomous driving.
  • This non-contact approach can improve driver safety and system usability in future vehicles.