Engineers at the University of New South Wales (UNSW) have developed a wearable sensor patch designed to continuously monitor heart and lung activity, offering a potential new tool for remote patient monitoring and chronic disease management.
Known as AusculPatch, the device is a lightweight sensor measuring approximately 20 x 47 x 3 millimetres and weighing 3.2 grams. Applied to the chest or over peripheral arteries, it captures subtle mechanical vibrations generated by the heart, lungs, blood flow and pulse waves. According to the research team, the patch is capable of detecting extremely low-frequency vibrations that are difficult to measure using existing wearable technologies while minimising interference from environmental noise.
The technology was evaluated through proof-of-concept studies published in Nature Communications. Researchers reported that the patch maintained the ability to clearly record heart sounds even in noisy environments, including during conversations and simulated background noise conditions. It also continuously captured cardiorespiratory data while participants carried out everyday activities such as walking, working, eating and climbing stairs.
Laboratory and early human testing demonstrated that AusculPatch readings were broadly consistent with established reference technologies, including electrocardiograms (ECGs), ultrasound systems, blood pressure monitors and digital stethoscopes.
The research team, led by Scientia Associate Professor Hoang-Phuong Phan, is collaborating with UNSW spin-off Apostele to explore the integration of acoustic and cardiopulmonary data into a broader digital health platform.
Discussing the project’s objectives, A/Prof Phan said, "A key focus of our current research is determining which signals are most clinically meaningful and how they can support decision-making without adding burden to healthcare providers."
Researchers believe the technology could support applications across chronic disease management, sleep monitoring and the detection of cardiovascular abnormalities outside hospital settings. The device also demonstrated the ability to detect vocal cord vibrations from the throat. Combined with machine-learning techniques, the system was able to recognise spoken words and wirelessly control a robotic arm, highlighting possible future applications for individuals with speech impairments or physical disabilities.
Looking ahead, the team plans to evaluate AusculPatch in approximately 200 patients with heart valve disease or implanted heart assist devices. A/Prof Phan explained that the study aims to "determine whether it can identify abnormal cardiopulmonary patterns, such as heart valve abnormalities that generate characteristic murmur sounds, and detect clinically meaningful changes over time, while remaining comfortable and practical for home use."
The resulting dataset will support the development of machine-learning-assisted diagnostic tools and advanced analytics. If future studies are successful, the researchers anticipate larger-scale clinical validation, regulatory submissions and potential integration into consumer healthcare and wellness products.
"Before large-scale deployment, further work is needed to validate the technology in larger patient populations, establish manufacturing scalability and regulatory approvals, and develop appropriate data governance and clinical implementation frameworks," A/Prof Phan added.
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