Researchers invent smart mask to track breath sounds for identification of respiratory disease

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Wearing face masks has been recognized as one of the most effective ways to prevent the spread of COVID-19, even in its upcoming endemic phase. Besides the conventional function of masks, the potential of smart masks to monitor human physiological signals is increasingly being explored. A research team led by the City University of Hong Kong (CityU) recently invented a smart mask, integrating a sound wave sensor based on an ultra-thin nanocomposite sponge structure, capable of detecting breathing sounds of breathing , cough and speech.

Using machine learning algorithms and a high-sensitivity sound wave sensor usable over a wide bandwidth, the smart mask has opened up new avenues for its application in the identification of respiratory diseases, as well as a tool for voice interaction. This ultra-lightweight wearable technology also has the potential to improve personal and public health by enabling prolonged and systematic monitoring of respiratory health in daily life.

A research team led by Professor Li Wenjung, Full Professor in the Department of Mechanical Engineering (MNE), Professor Wang Jianping, Professor in the Department of Computer Science (CS), and Dr. Yu Xinge, Associate Professor in the Department of Biomedical Engineering (BME) at CityU, recently developed this smart mask, which can detect and distinguish multiple breathing actions. The team of Professor Shen Jiangang from the School of Chinese Medicine at the University of Hong Kong also made an important contribution to the project. The results were published in Advanced Science under the title “Wide-bandwidth nanocomposite sensor-integrated smart mask for tracking multiphase respiratory activities.”

Importance of wearing masks even if COVID-19 becomes endemic

“Many countries now believe that COVID-19 will soon become endemic,” Professor Li said. to this disease in the years to come. It is important to remember that endemicity does not equal safety. He used malaria as an example to illustrate that although it is currently considered endemic in 87 countries, in 2020, it has infected an estimated 241 million people and caused 627,000 deaths, according to the World Health Organization.Thus, he suggested that people should continue to be cautious about COVID-19 and use available measures and proven, including masks, to control the spread of the virus.

“This smart mask uses our self-developed high-bandwidth, high-sensitivity flexible sensor that can detect and record daily human respiratory activity, such as breathing, coughing, and speaking for cloud data storage,” Professor Li explained.

The smart mask developed by the team has a sponge-like structure as thin as 400 μm, made with carbon nanotubes and polydimethylsiloxane (CNT/PDMS) materials, using the team’s new modified sacrificial release technique. . The ultra-thin and lightweight sensor can be virtually integrated and work effectively with both rigid masks and non-woven deformable fabric masks.

Good performance in static and dynamic pressure

The research team recruited 31 people to collect their respiratory activity while wearing the smart mask. The results showed that the acoustic wave sensor was very sensitive in measuring both static and dynamic pressure. In addition to performing well in the static pressure range of 27.9 Pa to 2.5 kPa, the sensor also responded to high frequency dynamic pressure generated by the human voice, i.e. the energy harmonic acoustics up to 4000 Hz. Additionally, the sensor can detect air movement, including directional flow and vibration. These results suggest that the sensor could be used to detect human respiratory activity by integrating it into a commercial polycarbonate mask. It also demonstrated that the smart mask could detect and differentiate between three common respiratory functions: breathing, coughing and speaking.

“Advanced artificial intelligence technology enables the built-in mask to automatically recognize different coughing and breathing patterns, indicating its potential use in diagnosing respiratory diseases in the future,” Professor Wang said. “Currently, researchers are using commercial sensors to detect temperature changes and airflow to count the number of coughs, but they cannot capture the important physiological information contained in the human voice, cough, and breath. Our smart mask is sensitive to both subtle air pressure and high-frequency vibration and can detect three cough phrases,” Professor Li added.

The team aims to eventually develop real-time diagnostic algorithms for applications such as symptom assessment of pneumoconiosis. “As a potentially low-cost everyday smart wearable device, this new IoT smart mask will help personal and public health management of respiratory disease screening and diagnosis, especially in high-population cities, such as Hong Kong,” said said Dr. Yu. The smart mask’s speech detection capability can also help solve the problem of sound attenuation caused by wearing masks.

The first co-authors of the paper are Miss Suo Jiao, Mr. Liu Yifan, and Dr. Wu Cong, all of whom are students of Professor Li. Corresponding co-authors include Dr. Yu, Professor Wang, and Professor Li by CityU. Other CityU team members include Dr. Walid Daoud and Dr. Yang Zhengbao from MNE and Dr. Li Xinyue from the School of Data Science.

The research was supported by the Shenzhen Municipal Science and Technology Innovation Commission, the Hong Kong Research Grants Council and the Hong Kong Center for Cerebro-cardiovascular Health Engineering.

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