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title: README
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We are building **Soma**, a foundation model for continuous physiological signals from wrist wearables. During training, sports video, audio, metabolic-chamber and lab measurements label the wristband's raw streams: PPG, accelerometer and skin temperature. At inference, the model needs only the wristband.
Our models will be published here.
## First release: Soma-ACC (coming soon)
A cross-device wrist accelerometer model. Every 30 seconds, it tells what a person is doing and whether they are asleep, from wrist motion alone. No camera, no microphone.
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| **Input** | Raw 3-axis wrist accelerometer data from any device. The model does not need to know which device. |
| **Output** | Every 30 seconds: an activity label scored against text descriptions of activities, plus a sleep probability. |
| **Training data** | Public datasets only. Every dataset is credited in the model card. |
| **Evaluation** | On people held out from training, with the device treated as unknown. |
## Principles
- **Public data, credited.** Every dataset we train on is named and credited in the model card.
- **Held-out people, unknown devices.** We report results on people the model never saw, with device identity hidden at test time.
- **Models produce the numbers; language models phrase them.** LLMs never generate physiological values.
- **Wellness and research, not medicine.** Our models are not medical devices and are not intended to diagnose, treat or prevent any disease.
## Related
- **[SomaLoop](https://somaticai.io/en/somaloop/)**: a screenless research wristband that streams raw PPG and accelerometer data.
Have reference measurements, such as sports video, metabolic-chamber data or lab tests? Let's pair them with continuous wrist signals: [contact@somaticai.io](mailto:contact@somaticai.io)