arXiv AI By Olivia Nocentini, Marta Lagomarsino, Gokhan Solak, Younggeol Cho, Qiyi Tong, Sara Zeynalpour, Marta Lorenzini, Alessandro Ledda, Arash Ajoudani

Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints

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Hugging Face Trending Papers
4d ago

GraTED: Graph--Tensor EEG Decoding for Passenger Hazard Perception in Autonomous Vehicles

GraTED is a Graph–Tensor EEG Decoding Model that preserves the spatial and temporal structure of passenger electroencephalography (EEG) data by modeling electrode positions as an undirected graph and applying graph convolution and low‑rank tensor classification. It is designed to predict risk perception and identify danger in autonomous‑vehicle scenarios. On the PassengerEEG dataset, GraTED outperforms seven baseline methods in balanced accuracy while using the fewest learnable parameters.

Hugging Face Trending Papers
Jul 16

InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring

Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion.

arXiv AI
Jul 17

InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring

arXiv:2607. 14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction.

By Hao Yang, Yanyan Zhao, Kewei Zhao, Hongbo Zhang, Tian Zheng, Yusheng Liu, Xing Fu, Bichen Wang, Yu Zhang, Hao He, Zhen Wu, Xuda Zhi, Yongbo Huang, Bing Qin
arXiv Computer Vision
Sep 4

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

The paper investigates the use of thermal infrared video to non‑invasively monitor cardiorespiratory and sudomotor activity in industrial human‑machine interfaces. It presents a signal‑processing pipeline that tracks facial regions, aggregates thermal signals, and separates slow sudomotor trends from faster heart‑rate and breathing‑rate components. Experiments on 31 driver‑monitoring sessions show that thermal imaging can estimate heart rate, breathing rate, and electrodermal activity with reasonable accuracy, while highlighting challenges such as ROI selection, polarity changes, latency, and subject variability.

By Constantino \'Alvarez Casado, Mohammad Rahman, Sasan Sharifipour, Nhi Nguyen, Manuel Lage Ca\~nellas, Xiaoting Wu, Miguel Bordallo L\'opez