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̽��ֱ��team, led by the ̽��ֱ�� of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pyjamas are worn loosely around the neck and chest.

̽��ֱ��sensors embedded in the smart pyjamas were trained using a ‘lightweight’ AI algorithm and can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. ̽��ֱ��energy-efficient sensors only require a handful of examples of sleep patterns to successfully identify the difference between regular and disordered sleep.

̽��ֱ��researchers say that their smart pyjamas could be useful for the millions of people in the UK who struggle with disordered sleep to monitor their sleep, and how it might be affected by lifestyle changes. ̽��ֱ�� are reported in the Proceedings of the National Academy of Sciences (PNAS).

Sleep is vital for human health, yet more than 60% of adults experience poor sleep quality, leading to the loss of between 44 and 54 annual working days, and an estimated one percent reduction in global GDP. Sleep behaviours such as mouth breathing, sleep apnoea and snoring are major contributors to poor sleep quality, and can lead to chronic conditions such as cardiovascular disease, diabetes and depression.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” said Professor Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated and isn’t suitable for long-term use at home.”

Home devices that are simpler than PSG, such as home sleep tests, typically focus on a single condition and are bulky or uncomfortable. Wearable devices such as smartwatches, while more comfortable to wear, can only infer sleep quality, and are not effective for accurately monitoring disordered sleep.

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality,” said Occhipinti.

To develop the smart pyjamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. ̽��ֱ��team re-designed the graphene-based sensors for breath analysis during sleep, and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” said Occhinpinti. “ ̽��ֱ��improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

̽��ֱ��researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnoea (CSA), and obstructive sleep apnoea (OSA). SleepNet is a ‘lightweight’ AI network, that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers.

“We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” said Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

̽��ֱ��smart pyjamas were tested on healthy patients and those with sleep apnoea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pyjamas with a special starching step, they were able to improve the durability of the sensors so they can be run through a regular washing machine.

̽��ֱ��most recent version of the smart pyjamas are also capable of wireless data transfer, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” said Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviours such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

̽��ֱ��researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

̽��ֱ��research was supported in part by the EU Graphene Flagship, Haleon, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang, Wentian Yi et al. ‘.’ PNAS (2025). DOI: 10.1073/pnas.2420498122