7 September 2026 | Puebla, Mexico
Time Paper ID Presentation
09:30–09:40 — Welcome and Opening Remarks – PAAIn Workshop
Session 1 – Challenges and Foundations in AI-Based Pain Assessment
09:40–09:55 152 AI4Pain2026 Challenge on Automatic Pain Assessment from Body Movement
09:55–10:10 168 The AI4Pain Grand Challenge 2026: A Multimodal Physiological Dataset and Benchmark for Pain Detection and Localisation
10:10–10:25 171 From Pain Detection to Pain Localisation: Exploring the Potential of Multimodal Physiological Signals
10:30–11:00 — Coffee Break
Session 2 – Computational Approaches for Pain Localisation
11:00–11:15 156 Noise-Cancelling Cross-Modal Fusion with Contrastive Localisation Objectives for Physiological Pain Site Classification
11:15–11:30 164 Nonlinear Physiological Descriptors and BiLSTM Networks for Pain Detection and Localization
11:30–11:45 166 Spatial Pain Recognition through Physiological Signal Fusion: A Deep Learning Perspective on Anatomical Localization
11:45–12:00 162 Assessing Pain Through Multimodal Physiological Signals Using Traditional Machine Learning in AI4Pain 2026
12:00–12:15 157 A Feature-Based Multimodal Approach to Pain Detection and Localisation Using Wearable Biosignals
12:15–12:30 150 Subject Aware Feature Modeling for Pain Localization: Normalization as the Dominant Inductive Bias
12:30–12:45 172 Pain You Can Detect, Location You Cannot: A Two-Stage Hierarchical Classifier for Pain Localisation
13:00–14:30 — Lunch
Keynote Presentation
14:30–15:30 — Affective Virtual Rehabilitation (including Q&A) – Prof Luis Enrique Sucar and Dr Jesús Joel Rivas
Session 3 – Learning Strategies for Pain Localisation
15:30–15:45 159 Subject-Independent Multimodal Pain Classification via a Calibrated XGBoost and Domain-Adversarial Neural Network Ensemble
15:45–16:00 170 Knowing Where It Hurts? Multimodal Spatio-Temporal Feature Engineering for Pain Location Classification
16:00–16:15 147 Two-Stage Cascade Classification for Pain State Recognition from Physiological Signals
16:15–16:30 142 Detection and Localization Are Different Tasks: Mechanism-Grounded Pain Recognition for the AI4Pain 2026 Challenge
16:30–17:00 — Coffee Break
Session 4 – Understanding Physiological Pain Localisation
17:00–17:15 169 An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
17:15–17:30 144 Pain Presence, Not Location: A Causal and Information-Theoretic Analysis of Pain Assessment from Peripheral Physiology
17:30–17:45 148 Architecturally Diverse Ensembling at the Peripheral-Signal Ceiling for Pain Localization
Session 5 – Winners Announcement
17:45–18:05 — AI4Pain Grand Challenge 2026 – Results and Performance Summary
18:05–18:20 — Awards and Closing Remarks
Presentation Information
Each accepted paper is allocated a 15-minute presentation slot, including questions and discussion. Presenters are encouraged to keep their presentations within the allocated time to ensure the workshop remains on schedule.
The AI4Pain Grand Challenge 2026 results and winners will be announced at the end of the workshop, followed by the awards and closing remarks.