At Oura, we are always innovating and improving our health sensing algorithms using a rigorous scientific process. We are committed to sharing our research findings, product validations, and company progress with our community.

Although the Oura Ring has already been validated against the gold standard sleep laboratory test — polysomnography (PSG) — our scientists and engineers continue to close the performance gap between traditional research tools and consumer wearables, and they’ve just achieved another pivotal milestone.

We are excited to announce new research, published in Sensors (the leading international, peer-reviewed, open-access journal on the science and technology of sensors), that showcases the results of Oura’s new sleep staging algorithm.

By leveraging every signal from the Oura Ring (movement, temperature, heart rate, & HRV), researchers were able to develop a new sleep staging algorithm that achieves 79% agreement with gold-standard PSG for 4-stage sleep classification (wake, light, deep, and rapid eye movement (REM) sleep). By contrast, most commercial wrist-based wearables are limited to 60-65% agreement.

Oura’s new sleep staging algorithm was developed using advanced machine learning techniques and one of the largest sleep datasets collected to date. This one-of-a-kind database spans multiple continents and demonstrates the power of using all of the Oura Ring’s signals to maximize accuracy.

Read the full paper in Sensors or see key takeaways below.

Taking A Global Approach

One of the main limitations of current academic research and commercial products is that sleep staging algorithms are developed on limited data (typically 20-30 nights) and uniform populations. Because these algorithms are “trained” on a narrow dataset, they may not perform the same when they’re extended to a broader, more diverse population.

To counteract that limitation, Oura is developing even more accurate sleep staging algorithms by actively building one of the largest sleep datasets to-date. The dataset has already reached 440 nights of PSG and Oura Ring data from sleep labs all over the world.

This dataset will continue to grow and has already enabled Oura to train and evaluate algorithms across diverse individuals. Notably, the database contains diverse sleeping patterns, including physiological responses, across:

  • Different environments (e.g. weather, climate)
  • Unique behavioral factors (e.g. eating habits that could influence sleep, or different wakeup times and bedtimes)
  • The full age spectrum (15-73) to account for sleep staging changes that come with age (e.g., deep sleep decreases with age)

Using Every Metric

Most wearables use a combination of different body signals (e.g. heart rate, movement, etc.) to determine when you’ve fallen asleep and which sleep stage you’re currently in. Each sleep stage shows a distinctive pattern. For example, during REM, your heart rate and respiratory rate increase while your body’s temperature drops and your muscles relax to prevent you from acting out any dreams.

So, if you want to get a clear look at what’s happening on the inside after you’ve fallen asleep, the quality and accuracy of your sleep insights will depend upon your device’s ability to correctly measure and identify these distinct patterns.

Oura’s unique ability to leverage movement, temperature, heart rate, heart rate variability (HRV), and mathematical modeling that incorporates known sleep patterns (e.g, REM sleep tends to occur later in the night) results in state-of-the-art sleep staging accuracy. The example below shows how closely Oura is able to match gold-standard PSG for a typical night.

Read the full paper in Sensors for more insights on the relation between temperature, HRV, and different sleep stages.

Maintaining Accuracy Across Sleep Stages

One of the main strengths of this work is the high sensitivity and specificity the algorithm achieves across all sleep stages — ranging from 74% to 98%. While other studies have shown similar results for the detection of a specific sleep stage, this improved performance typically comes at the expense of the others (e.g. high performance in detecting deep sleep might result in a poor ability to detect REM).

This research demonstrates that a sophisticated model can maintain high sensitivity and specificity for all sleep stages.

Read the full paper in Sensors for additional box plots and graphs.

What’s Next?

Lead author, Marco Altini, shares his excitement for next steps, collaborating with Oura:

“The sleep staging algorithm we developed with this dataset is actively being adjusted so it can be unlocked on the Oura Ring for consumers in the coming months. We’ve done most of the work already: collecting a large dataset across the world, building key features relying on high-quality data from the ring’s sensors, and developing a machine learning algorithm outperforming the current state of the art tools. The last key step is to move the new algorithm to the Oura Ring. As scientists, we use high-level tools that allow us to iterate and validate models at unprecedented speed without worrying about computational power. Now our engineers are refining the algorithm so that it requires less memory and resources, with the goal of pushing this forward to consumers as fast as possible.” 

In the meantime, we will continue to work directly with experts to advance sleep research and, of course, keep the Oura Community updated each step of the way.

“We see a bright future where sleep research isn’t just confined to an occasional and expensive visit to a sleep lab. A future with transparency where individuals have access to the ‘how, what, and why’ of their sleep data and actionable insights. A future where consumer wearables like Oura, that have undergone rigorous clinical validation, can help people own their health and improve their sleep.”  – Shyamal Patel, Head of Science at Oura

Responses From The Research Community

“In a world where wearable sleep trackers have been commoditized, it is great that one company has taken the trouble not only to validate its products across multiple age groups but also reveal to sleep professionals the methodology underlying their sleep stage estimation. Oura stands out as being dedicated to proving and improving its craft. My team and I are delighted to be part of their transformation of sleep, health and wellbeing.”

– Michael Chee, Professor and Director, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore.

“This most recent paper evaluating the performance of the Oura Ring in measuring sleep is a wonderful example of what can be achieved when deep knowledge of sleep science is combined with innovative technology and state of the art modeling techniques. The combination of the technical performance and extremely usable form factor makes the Oura Ring a potential savior for the problem of how to ubiquitously measure sleep in large numbers of people. It represents a real breakthrough for the field of sleep science and potentially for sleep medicine.”

-Ian Colrain, President, SRI Biosciences and Professorial Fellow, School of Psychological Sciences, The University of Melbourne