A smart sensor for your muscles and tissues

Engineers at Duke University have developed a wireless patch that can non-invasively measure skin and tissue stiffness at depths of up to a couple of inches. Already smaller than a smartwatch, the device could be a gateway into a wide array of medical applications such as the monitoring of wound healing, chronic conditions like skin cancer, fluid management during resuscitation efforts and muscle rehabilitation.

With further refinement, the researchers plan to create versions of the technology that could seamlessly integrate with athletic apparel to provide real-time feedback of muscle performance. This could allow athletes to optimize training sessions, spot fatigue before it turns into injury and fine-tune recovery schedules.

The research appears online September 3 in the journal Science Advances.

“I recently had my first child and discovered that I could use this device to track my body’s supply of milk in real-time,” said Xiaoyue Ni, assistant professor of mechanical engineering and materials science at Duke. “Nobody has ever created a tissue stiffness monitor like this before, so the potential use cases are truly limitless.”

Tissue stiffness is an important piece of information in many medical situations. When needed, clinicians currently use bulky and expensive ultrasound machines to take these measurements. The information can be used for a wide variety of applications ranging from diagnosing cancer to rehabilitating acute muscle injuries.

The prototype for the new device achieves similar functions for tissues up to a couple of inches deep. Currently about the size of a watch face, the patch is powered by batteries that can last for a couple of hours and can be further upgraded to multiple days, wirelessly communicates with devices via Bluetooth and can be temporarily stuck anywhere on a person’s skin.

It works similarly to how a person searches for a wall stud to hang a picture. Tapping on areas without a stud produces a lower-pitched noise than tapping on a wall directly on one.

Similarly, the new technology measures tissue stiffness by sending sound waves along the surface of the body and listening to the resulting vibrations. Because lower frequencies travel deeper into tissue than higher frequencies, the device sweeps between 50 Hertz (roughly the sound of deep thunder) to 800 Hertz (more like an ambulance siren) to measure various depths. It then separates the results into two layers to differentiate between skin and the underlying tissue.

“Creating this automated two-layer model analysis and integrating it into a system-level design has been the most challenging part of this project,” said Chenhang Li, a fifth-year PhD student in Ni’s lab, who has been working on the approach since day one of his PhD. “And then we had to figure out how to do the signal processing in real-time and complete a large number of validation tests. It’s been a long time coming.”

With the proof-of-concept results in hand, Ni and Li are now working to discover and pursue the best use cases for their invention.

“We envision future versions built right into sports gear, medical wraps, everyday clothing or assistive robotics, creating a continuous ‘health dashboard’ for your body’s hidden layers,” Ni said. “And it will be as easy to wear as a smartwatch, but far more powerful.”

Co-corresponding authors on the research include John Rogers, director of the Querrey Simpson Institute for Bioelectronics at Northwestern University, and Changsheng Wu, assistant professor of materials science and engineering at the National University of Singapore.

This research was supported by the National Institutes of Health (R21EB034973), the National University of Singapore Presidential Young Professorship, and the Ministry of Education, Singapore, under the Academic Research Fund Tier 1 (FY2023 & FY2024).

“Wireless, wearable elastography via mechano-acoustic wave sensing for ambulatory monitoring of tissue stiffness.” Chenhang Li, Heling Wang, Ziwu Song, Wei Zhang, Yuxin Pan, Zihao Zhao,Chaorui Qiu, Kaiping Yin, Mengdi Han, Allison Bingqing Wang, Haiwen Luan,Jiahong Li, Wenyuan Yan, Shulin Chen, Haixu Shen, Tzu-Li Liu, Sabrina S.M. Lee, Wenbo Ding, Yonggang Huang, John A. Rogers, Changsheng Wu, and Xiaoyue Ni. Science Advances, 2025. DOI: 10.1126/sciadv.ady0534