Behavior and Emotion Analysis through wearable Technology (BEAT)

BEAT Keynotes Call for Papers Important Dates Organizers Return To Top

Workshop Description

Wearable movement and physiology sensors such as smartwatches, smart glasses, and ear-buds offer lightweight, non-invasive, and ecologically valid means to monitor human activity, affective state, and social behavior. With the rise of commercially deployed devices and new wearable foundation models, opportunities for scalable human behavior analysis continue to grow. However, challenges such as personalized modeling, on-device integration, or multimodal fusion prevail, limiting in-the-wild deployment of wearable devices.

The 1st Workshop on Behavioral and Emotion Analysis through wearable Technology (BEAT) aims to foster collaboration between ML researchers from various backgrounds (Gesture & Face Analysis, Affective Computing, HRI), as well as researchers in biomedical engineering. The main focus is on lightweight wearable movement and physiological sensors for computational human behavior analysis. While contributions are expected to be centered on real-world and ecologically valid settings, we also welcome controlled laboratory studies that introduce novel sensing approaches, benchmark datasets, or innovative applications.

Participation in this workshop will take place in person during the IEEE FG 2026, held in Kyoto from 25-29 May 2026.

Keynotes

Aaqib Saeed, Eindhoven University of Technology (TU/e), the Netherlands, is an Assistant Professor (tenured) at Eindhoven University of Technology (TU/e). He holds a Ph.D. cum laude from TU/e, where he conducted research in the Department of Mathematics and Computer Science, on self-supervised learning for 8 sensory data (ECG, EEG, IMU, PPG and Audio). Aaqib Saeed studies Artificial Intelligence at the intersection of Self-Supervised Learning, Sensing Systems, and Decentralized Computing. His work explores intelligent systems that can learn from raw sensory signals (ECG, EEG, Audio, Speech, IMU) without extensive human supervision, enabling scalable AI solutions for healthcare and beyond.

Zilu Liang, Kyoto University of Advanced Science (KUAS), Japan, is an Associate Professor at KUAS, where she leads the Ubiquitous and Personal Computing Lab. She received her Ph.D. and M.Sc. in Electrical Engineering and Information Systems from the University of Tokyo. Before joining KUAS, she held research and academic positions at the University of Tokyo, the University of Melbourne, the University of Oxford, and Imperial College London. Her research focuses on human-centered computing, wearable and ubiquitous technologies, and AI-driven methods for understanding and supporting human behavior.

Kai Kunze, Keio University (KMD), Japan, works as Professor at the Graduate School of Media Design, Keio University, Yokohama, Japan. Beforehand, he held an Assistant Professorship at Osaka Prefecture University. With over 254 papers published at high profile conferences and journals (e.g. CHI, TOCHI, UIST, IEEE Computer), Kai Kunze is a pioneer researcher in the HCI field, augmenting humans using technology. His most significant research contributions are in Eyewear Computing and Placement Robust Activity Recognition. His current research also includes Digitalizing Human Emotions and Amplifying Human Senses.

Call for Papers

We welcome submissions to two tracks:

Main Track (Original Research): This track accepts original and unpublished work of 4 to 8-pages (excluding references). All submissions will undergo a double-blind review process, with three reviewers assigned to each paper. Accepted papers will be included in the workshop proceedings, and presented as posters or oral presentations.
Non-Archival Track (Published Work): Authors of papers previously published in other conferences or journals are invited to submit a 1-page summary to present their work, which will not be included in the proceedings. These submissions will be evaluated and selected by the organizers based on quality and relevance to the workshop topics.

Topics of interest include, but are not limited to:

Machine Learning and computational models for movement and physiological wearables
Resource efficient and lightweight models
Multimodal fusion and synchronization strategies
Methods for irregularly sampled or missing data
Individual differences, personalization and context-awareness
Ethical and privacy-preserving AI in wearable systems
Novel wearables and applications
Experimental methods for validation of wearable systems
Lab-controlled experiments and In-the-wild deployment
Datasets and Benchmarks
Responsible data management and user consent
Applications in Affective Computing / Mobile Health / Action Recognition / Social Interaction / HRI