HMN 2026: How Touch-free health monitoring could breathe new life into health diagnostics

A new development in wireless sensing technology that can reliably screen for five common pulmonary diseases could lead to new forms of touch-free diagnostics.

An international team of engineers and computing scientists based in Scotland and Pakistan developed the system. They say their findings could lead to new forms of personalized health monitoring both in clinical settings and in the “smart homes” of the future.

How the wireless screening system works

The system, showcased in a new paper published in the journal Communications Medicine, uses radio signals of multiple frequencies paired with sophisticated artificial intelligence to recognize the characteristic breathing patterns of five common lung diseases.

It works by exposing patients to harmless microwave signals emitted by a pair of software-defined radios at 5.23Ghz—a frequency at the lower end of the bands expected to be used by future 6G and WiFi7 networks. AI-enabled analysis of the signals reflected from the patients’ chests allows the system to identify the breathing patterns that are caused by different lung disorders.

Testing and accuracy of the technology

In lab tests using real-world radio data, the system was able to accurately screen for asthma, chronic obstructive pulmonary disease, interstitial lung disease, pneumonia and tuberculosis with 98% accuracy.

The team tested their system by gathering microwave reflection data on 190 patients diagnosed with various respiratory diseases at a local hospital in Lahore, Pakistan. They chose the data collection window to be during the high-smog season between October 2023 to January 2024, and again in January 2025. They also gathered data from 30 healthy individuals to act as a control group.

The team recorded nearly seven and a half hours of microwave-frequency data from the study’s participants. They analyzed the data using five different machine learning models and three deep learning models to determine which performed best at correctly classifying the patients’ conditions. A deep learning model called vanilla CNN delivered the best performance, correctly spotting the signs of specific illnesses 98% of the time, and identifying the healthy individuals with 100% accuracy.

Expert perspectives and future potential

Professor Qammer H. Abbasi, director of the University of Glasgow’s Center for Integrated Sensing and Communication Enabling Cognitive Cities, led the research and is one of the paper’s corresponding authors. He said, “The ultrafast 6G wireless communications networks of the future have the potential to do integrated sensing and communication (ISAC), which will unlock a wide range of benefits for people around the world, with health care being one of the key applications.

“This research showcases the effectiveness of ISAC, which allows a single communications infrastructure to both transmit data and perform sensing tasks at the same time. The sophisticated sensing that underpins our results only took up 12.5% of the system’s available bandwidth. That means that the rest of the system’s bandwidth could be used for data transmission to help enable future generations of integrated, continuous health monitoring devices.”

Professor Muhammad Mahboob Ur Rahman, of the Information Technology University in Pakistan, is another of the paper’s corresponding authors. He said, “Being able to do accurate, low-cost, and swift mass screening of people for their respiratory health in a non-clinical and resource-constrained setting, without requiring them take invasive tests such as spirometry or uncomfortable radiations such as X-rays and CT scans could be a game-changer for health care delivery, especially amidst the outbreak of a pandemic.

“By combining AI with radio sensing in a 6G/WiFi framework, we’ve been able to accurately spot the signs of lung disease without the need for physical contact, stethoscopes, or imaging scans. That could help enable safe, continuous, and contactless screening and early detection of anomalies, which has the potential to reduce health care costs due to early medical intervention.

“This work is also anticipated to have a big impact in low-resource settings and during future outbreaks of infectious diseases like COVID-19, where reducing contact with patients could help limit the spread of infections.”

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