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An open-source end-to-end machine learning platform for edge devices (MCUs) that simplifies data labeling and deployment, featuring an auto-labeler for time-series sensor data and a chatbot for data insights.
StepFM is a foundation model that uses only step counter data for broad-spectrum health prediction, offering a privacy-preserving and scalable alternative to high-frequency sensor models.
Forgis Labs presents a family of foundation models for time series sensor data in industrial settings, with five papers accepted to ICML 2026 workshops, enabling event prediction and natural language explanation from raw sensor streams.
Google researchers propose SensorFM, a foundation model trained on over 1 trillion minutes of unlabeled wearable data from 5 million people, which learns general physiological patterns and outperforms engineered features on 34 of 35 health prediction tasks.