Sleep smarter, dream deeper

SomNyx leverages deep-learning technology within a first-of-its-kind strain-sensing garment to analyse sleep states, with very high accuracy. This distinctive approach supports personalised and precise sleep analysis using the same device across care settings—from hospital, to rehabilitation, to home.

 

Building on our 2025 study published in the Proceedings of the National Academy of Sciences, our 2026 study in IEEE Transactions on Neural Systems and Rehabilitation Engineering evaluated the approach in an expanded cohort of 24 participants and assessed its performance in the presence of motion artefacts.

SomNyx enables the comfortable, convenient, and effortless capture of rich, real-world datasets across a wide range of mobility levels. Our solution aims to streamline patient monitoring through automated data processing, increase the depth and relevance of sleep tracking across multiple nights of wear, and support earlier identification of sleep problems and neurological disorders.​

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The SomNyx wearable is currently undergoing clinical validation and is not yet available for pre-order. Sleep physicians interested in joining our early-adopter programme are invited to get in touch using the form below.

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References

[1] Tang, C., Yi, W., Xu, M., Jin, Y., Zhang, Z., Chen, X., Liao, C., Kang, M., Gao, S., Smielewski, P., & Occhipinti, L. G. (2025). A deep learning–enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life. Proceedings of the National Academy of Sciences, 122(7), Article e2420498122. https://doi.org/10.1073/pnas.2420498122

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[2] Tang, C., Li, X., Qi, L., Yi, W., Zhang, Z., Occhipinti, E., Deng, S., Pan, Y., Occhipinti, L. G., & Gao, S. (2026). Robust sleep behavior monitoring under motion artefacts via motion-conditioned learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Advance online publication. https://doi.org/10.1109/TNSRE.2026.3715694

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