Valentina Becchetti, Muhammad Umar Khan, Stefanos Gkikas, Alessandro Giuseppi, Raul Fernandez Rojas
Automated pain recognition is a fundamental challenge for the advancement of clinical care and empathetic human-computer interaction, due to the subjective and dynamic nature of pain. In this study, we propose a deep learning framework for objective pain assessment based on multimodal physiological signals from the AI4Pain dataset. The approach focuses on Blood Volume Pulse (BVP) and Respiration (RESP), capturing complementary aspects of autonomic responses to nociceptive stimulation. To explicitly model their temporal dynamics, the signals are transformed into structured trajectory representations using Hankel matrix embeddings, enabling the learning of dynamical patterns beyond conventional time-series representations. These embeddings are processed by modality-specific Fully Convolutional Networks, and their outputs are integrated through a late fusion strategy for three-class classification (No Pain, Low Pain, High Pain). Experimental results demonstrate that the proposed method achieves an accuracy of 67.09% and an F1-score of 66.41%, providing competitive performance compared to existing approaches, while relying on no handcrafted feature engineering. Multimodal fusion significantly outperformed either modality alone, and the Hankel embedding showed a trend towards further improvement. Further validation is required to establish generalisability to broader populations and clinical settings.
Stefanos Gkikas, Thomas Kassiotis, Yu Fang, Christian Arzate Cruz, Miao Cheng, Zongyi Jiang, Zacharias Kamarianakis, Houshyar Asadi 等 11 位作者
Stress is a leading contributor to long-term physical and mental health decline. The automatic assessment of stress is a promising approach for continuous monitoring and mitigating human biases. However, the development of automated systems remains challenging due to the heterogeneity of physiological and behavioral signals. This paper presents MUPA${}^2$S, a multimodal framework for automatic stress assessment that combines facial video and ECG signals within a unified perception architecture. Both modalities are mapped into a shared token representation without modality-specific components, and processed through asymmetric attention comprising segment-local cross-attention and global self-attention across segment states. Experiments on a newly collected 58-subject stress dataset, evaluated under a stratified subject-level protocol, show that facial video achieves 69.44% test accuracy, while ECG-only performance remains close to chance under the proposed representation. Unified Video-ECG fusion achieves a test accuracy of 69.58% at approximately half the number of parameters of feature-level fusion, with negligible additional computational cost. Feature-level fusion does not exceed the video-only baseline, while unified input-level integration provides a parameter-efficient mechanism for jointly processing the two modalities.