Oura sued over sleep-tracking accuracy claims

A proposed class action lawsuit filed Thursday in San Francisco accuses smart ring maker Oura of deceiving consumers about the accuracy of its sleep-tracking features. The suit, filed by Clarkson Law Firm, alleges that Oura rings cannot measure any of the physiological signals needed to assess sleep quality or determine sleep stages, and instead rely on AI-generated estimates that have “a coin flip’s chance of being correct.” The complaint argues that measuring the exact stage of sleep a wearer is in would, in reality, require electrodes on the scalp and sensors on the eyes — the kind of setup only a hospital or clinical sleep lab can provide — even though Oura sold rings priced at $300 and up while advertising that capability.

The lawsuit follows years of Oura users complaining online about the ring’s sleep accuracy, with some saying they felt they had slept poorly but were told their sleep had been optimal, and others doubting the device’s ability to detect sleep cycles at all. Despite that, the complaint alleges Oura marketed the ring as highly accurate — claiming, at various points, 79% accuracy and, more recently, 95% sleep-staging accuracy compared with clinical sleep labs. The complaint’s core argument is that meaningful sleep staging is a function of brain activity, which a finger-worn sensor cannot observe.

The suit seeks to stop Oura from advertising capabilities the complaint says its rings don’t deliver, along with injunctive relief and restitution for affected buyers. Oura did not immediately respond to TechCrunch’s request for comment. Ryan Clarkson, the law firm’s co-founder and managing partner, argued that marketing an inaccurate sleep tracker as precise is dangerous specifically because people believe the numbers and change their behavior around them.

Why it matters

As wearables increasingly market themselves as at-home substitutes for clinical diagnostics, the case tests how much accuracy a device actually needs to back up a health claim — and how far off an AI-estimated metric can be before it becomes false advertising.