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Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
Towards Open-World Gesture Recognition
We propose leveraging continual learning to make machine learning models adaptive to new tasks without degrading performance on previously learned tasks.
From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health.
