Learning Transferable Sensor Models via Language-Informed Pretraining
Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured from diverse healthcare sensors. However, existing sensor-language models can only process a fixed number of sensor channels at a fixed sampling rate, making them difficult to adapt to new or different sensor configurations. This inflexibility prevents models from generalizing across heterogeneous health sensing ecosystems, where data spans high-frequency wearable streams to sparse, day-scale clinical signals. To bridge this gap, we introduce SLIP (Sensor Language-Informed Pretraining), an open-source framework for learning language-aligned representa...
| Authors | Yuliang Chen, Arvind Pillai, Yu Yvonne Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 March 2026 |
| Last updated at source | 27 September 2026 |
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