Google introduces SensorLM, a family of models for analyzing wearable data patterns
Google has launched SensorLM, a new family of models designed to analyze data from wearable devices. This AI system translates raw sensor data into understandable health descriptions, utilizing a vast dataset of nearly 60 million hours of information. SensorLM aims to improve activity recognition and physiological state summarization without the need for labeled training data.
- ▪SensorLM is built on 59.7 million hours of data from Fitbit and Pixel Watch devices.
- ▪The model can identify user activities without explicit training on labeled examples.
- ▪It surpasses previous methodologies in sensor understanding tasks and shows strong performance scaling.
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Google introduces SensorLM, a family of models for analyzing wearable data patterns Built on nearly 60 million hours of Fitbit and Pixel Watch data, Google's new AI translates raw sensor readings into plain English health descriptions. Share Add us on Google by Editorial Team May. 23, 2026 window.sevioads = window.sevioads || []; var sevioads_preferences = []; sevioads_preferences[0] = {}; sevioads_preferences[0].zone = "01f21ccf-2092-46b1-9ac7-8c44cc782e0f"; sevioads_preferences[0].adType = "native"; sevioads_preferences[0].inventoryId = "c5700508-581b-472c-8fdd-a931cdbfc8e1"; sevioads_preferences[0].accountId = "1e47efc1-ec2d-4fca-a8b9-354e249e5095"; sevioads.push(sevioads_preferences); Google Research has unveiled SensorLM, a family of foundation models designed to take raw data…
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