Jiening Zhan
Jiening Zhan has 2 papers in the ChatPPG research atlas (2024–2025), linked to 2 models and 24 co-authors in the atlas. Main topics: accelerometer, multimodal, sensor-language-model, ppg, foundation-model, IMU.
Papers
- SensorLM: Learning the Language of Wearable Sensors2025 · arXiv preprint
SensorLM is a family of sensor-language foundation models that describe wearable data in natural language. A hierarchical captioning pipeline generates statistical, structural and semantic descriptions of the sensor data. The models show scaling behaviour, label efficiency, sensor captioning and zero-shot transfer to new tasks.
- Published: arXiv preprint, 2025
- Cohort: 103,000 people
- Method: hierarchical sensor caption generation
- Scaling Wearable Foundation Models (LSM)2024 · ICLR 2025
LSM (Large Sensor Model) is a Google study of how wearable foundation models scale with compute, data and model size. It used up to 40 million hours of per-minute heart rate, HRV, electrodermal activity, accelerometer, skin temperature and altimeter data from more than 165,000 people. The authors report scaling laws for imputation, interpolation and extrapolation, and show sample-efficient learning for exercise and activity recognition.
- Published: ICLR 2025
- Data: up to 40 million hours, 165,000+ people
- Signals: HR, HRV, EDA, accelerometer, skin temperature, altimeter