New foundation model analyzes wearable data for mental health insights

Researchers have developed a new foundation model called PAT (Pretrained Actigraphy Transformer) specifically for analyzing wearable movement data in mental health research. This open-source model uses self-supervised learning on actigraphy sequences to predict psychiatric outcomes, outperforming traditional time-series models. PAT demonstrated significant improvements in predicting benzodiazepine use, depression, and sleep abnormalities, while also offering interpretable attention maps to highlight key activity periods. AI

IMPACT Enables more accurate and interpretable analysis of wearable sensor data for mental health research.

RANK_REASON The cluster contains an academic paper detailing a new model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Franklin Ruan
• Mental health research
• PAT
• Wearable movement data

• paper
• model release
• product

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