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A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods
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A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods
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From the original abstract: This thesis initially aims to study the pain assessment process from a clinical-theoretical perspective while exploring and examining existing automatic approaches. Building on this foundation, the primary objective of this Ph.D. project is to develop innovative computational methods for automatic pain assessment that achieve high performance and are applicable in real clinical settings. A primary goal is to thoroughly investigate and assess significant factors, including demographic elements that impact pain perception, as recognized in pain research, through a computational standpoint. Within the limits of the available data in this research area, our goal was to design, develop, propose, and offer automatic pain assessment pipelines for unimodal and multimodal configurations that are applicable to the specific requirements of different scenarios. The studies published in this Ph.D. thesis showcased the effectiveness of the proposed methods, achieving state-of-the-art results. Additionally, they paved the way for exploring new approaches in artificial intelligence, foundation models, and generative artificial intelligence.
Forward citations
Cited by 8 Pith papers
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One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
A minimal one-block transformer architecture classifies cognitive workload from EEG recordings with high accuracy while using under 0.5 million parameters and 0.02 GFLOPs.
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An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
On the AI4Pain 2026 dataset, Extra Trees with 115 hand-crafted features (macro-F1 0.539) beats deep sequence models (0.465), and pain localization (0.552) is far harder than pain detection (0.815).
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ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment
Reorganizing facial video into four channel-concatenated quadrants before tokenization yields 56.00% test accuracy on AI4Pain video-only pain classification, the highest reported under that benchmark protocol.
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A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities
A unified tokenizer maps facial video and fNIRS into one token space; the segment-latent transformer hits 57.33% test accuracy on AI4Pain pain recognition.
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One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
A lightweight one-block transformer architecture for EEG-based cognitive workload classification that uses under 0.5 million parameters and 0.02 GFLOPs.
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A Lightweight Transformer for Pain Recognition from Brain Activity
A lightweight transformer fuses multiple fNIRS representations via unified tokenization to achieve competitive pain recognition on the AI4Pain dataset while remaining computationally compact for real-time use.
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A Lightweight Transformer for Pain Recognition from Brain Activity
Lightweight transformer fuses raw and spectral fNIRS representations via unified tokenization for competitive pain recognition on the AI4Pain dataset while remaining computationally compact.
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A Lightweight Transformer for Pain Recognition from Brain Activity
A lightweight transformer fuses multiple fNIRS signal views through shared tokenization to achieve competitive pain recognition on the AI4Pain dataset while staying computationally compact.
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