你的呼吸方式就像指纹一样,可以识别你的身份。
How you breathe is like a fingerprint that can identify you

原始链接: https://www.nature.com/articles/d41586-025-01835-0

近期一项研究表明,个体呼吸模式可能与指纹一样独特,这为身份识别以及深入了解身心特征提供了一种新的方法。魏茨曼科学研究所的研究人员研发了一种可穿戴设备,用于测量97名健康个体24小时内鼻腔气流。通过分析吸气/呼气时长和气流不对称性等参数,他们训练了一种机器学习算法,仅根据呼吸模式就能识别参与者。 该算法在数周或数月后重新识别参与者时达到了很高的准确率。有趣的是,这项研究还发现呼吸模式与体重指数(BMI)以及自我报告的抑郁症和焦虑症水平之间存在相关性。这意味着清醒和睡眠期间的呼吸模式可能作为一种诊断工具,用于识别个体并揭示其身心健康信息。

Hacker News 正在讨论一篇文章,该文章声称呼吸模式可以像指纹一样唯一地识别个人。一项研究表明,其准确率高达 96.8%,即使跨越数周或数月也能保持。评论者探讨了这项技术对隐私、安全甚至工作场所互动(尤其是在共享洗手间空间)的影响。人们对潜在的监控以及通过呼吸被识别的可能性表示担忧。一些人提出了幽默的应对措施,例如随机化呼吸模式或携带一只吉娃娃。另一些人则思考了其实际应用,例如医疗诊断或身份验证,尽管目前的方法需要使用鼻导管。讨论还涉及到相关的主题,例如睡眠呼吸暂停(CPAP)、使用传感器检测呼吸以及压力对呼吸模式的影响。
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原文
Hundreds of yoga enthusiasts hold cobra pose in a park.

Every breath you take ... could add to a breathing pattern that is unique to you, a study finds.Credit: Anusak Laowilas/NurPhoto via Getty

Like the swirls in fingerprints, a person’s breathing pattern might be unique to them — offering a way not only to identify individuals, but also to identify some of their physical and mental traits.

A team of researchers measured the breathing of 97 healthy people for 24 hours, and found that they could identify participants with relatively high accuracy from their breathing pattern alone. What’s more, they found that these patterns can be correlated with body-mass index (BMI) and signs of depression and anxiety.

“In a way, we’re reading the mind through the nose,” says co-author Noam Sobel, a neurobiologist at the Weizmann Institute of Science in Rehovot, Israel. “This could be a very powerful diagnostic tool.” The team published its study today in Current Biology1.

Taking a breath

Breathing is deeply connected to the brain. Every inhalation and exhalation is coordinated to supply the oxygen needed for the brain to manage the body’s systems. Sobel and his team wondered: if every brain functions differently, shouldn’t every person’s breathing be unique, too?

To test this, the researchers developed a custom, wearable device that records airflow through each of a person’s nostrils. Mounted on the back of the neck, the device, which has tubes fitted under the nose, tracks people’s breathing during their everyday routines, both while they are awake and while they are asleep.

Top: close-up of a person's nose with breathing tubes; bottom: a small silicon-encased device on the person's neck, connected to breathing tubes.

Researchers measured study participants’ breathing patterns over 24 hours, using a custom device that sits on the back of the neck.Credit: Soroka et al., Current Biology

To characterize a person’s breath pattern, the team extracted 24 parameters from the airflow data, including duration of inhalation and exhalation and airflow asymmetry between nostrils. They separated the periods when participants were awake and asleep, and trained a machine-learning algorithm with the data.

When 42 of the participants came back to the laboratory weeks, months and even two years later, to take part in another 24-hour measurement, the trained algorithm could identify them from their breath patterns. Data from periods when the participants were awake gave more accurate results than did those from sleeping periods, but when the researchers used a 100-parameter characterization of a full data set instead of one using 24 parameters, they could pick individuals out with 96.8% accuracy.

Given this success, Sobel and his colleagues began wondering whether they could learn more from the breath patterns.

Healthy breathing

The researchers collected data on the participants’ BMIs, and from questionnaires that assess levels of depression and anxiety. An analysis found correlations between this information and the breathing patterns, even though most participants had low-level scores on the questionnaires.

For instance, the breathing profiles during sleep of people with higher BMIs were different from those of people with lower BMIs. And those who scored higher on the questionnaires for anxiety or depression had distinct patterns in how they inhaled and exhaled.

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