Learning Transferable Sensor Models via Language-Informed Pretraining | ScienceToStartup | ScienceToStartup

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References (45)

[1]

A multimodal sleep foundation model for disease prediction

2026R. Thapa, M. R. Kjaer et al.

[2]

Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices

2025Minghui Qiu, Cekai Weng et al.

[3]

LSM-2: Learning from Incomplete Wearable Sensor Data

Founder's Pitch

"SLIP is an open-source framework that learns language-aligned representations for diverse sensor setups, enhancing zero-shot transfer and generative reasoning."

Sensor Data Processing•Score: 7•View PDF ↗

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Learning Transferable Sensor Models via Language-Informed Pretraining

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References (45)

Founder's Pitch

"SLIP is an open-source framework that learns language-aligned representations for diverse sensor setups, enhancing zero-shot transfer and generative reasoning."

Commercial Viability Breakdown

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Learning Transferable Sensor Models via Language-Informed Pretraining

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References (45)

Founder's Pitch

"SLIP is an open-source framework that learns language-aligned representations for diverse sensor setups, enhancing zero-shot transfer and generative reasoning."

Commercial Viability Breakdown

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