New: MAISON-LLF Data Challenge
In this workshop, we invite previously unpublished and novel submissions in the format of regular papers, in the following areas (pertaining to aging and rehabilitation), but not limited to:
End-to-end data pipelines for aging and rehabilitation research, including multimodal data collection, annotation, curation, sharing, and harmonization.
AI and machine learning methods for continuous, real-time, and long-term monitoring of older adults using wearable, ambient, audio, video, and clinical data.
Learning from limited, noisy, imbalanced, and weakly labeled data in aging and rehabilitation settings.
Anomaly, novelty, and rare-event detection for safety-critical behaviors (e.g., falls, agitation, wandering).
Machine learning and deep learning methods for modeling physical, cognitive, and mental health trajectories (e.g., frailty, dementia, mobility, mental health).
Trustworthy, robust, and deployable AI systems, including uncertainty estimation, reliability, and failure detection in real-world care environments.
Explainable and interpretable AI for clinical decision support and personalized care.
Privacy-preserving and decentralized learning approaches, including federated learning, on-device learning, and differential privacy.
Fair, inclusive, and ethical AI for aging populations, addressing biases related to age, gender, ethnicity, and socioeconomic factors.
Human-in-the-loop and clinician-in-the-loop AI systems for rehabilitation, monitoring, and intervention.
Foundation models, large language models, generative AI, retrieval-augmented generation (RAG), and agentic AI for aging and rehabilitation, including personalization and adaptation to individual needs.
Synthetic data generation, simulation, and data augmentation techniques for rare events and underrepresented populations.
Natural Language Processing (NLP) and multimodal interaction techniques for communication, monitoring, and engagement in elderly care.