Wearable Ultrasound for Deep Tissue Monitoring

Researchers at the University of California San Diego have created a wearable ultrasound system that can monitor deep tissues, as far as 16.5 cm (6.5 inches) below the surface of the body. Moreover, the team employed a machine learning algorithm to reduce the noise associated with movement, helping to obtain reliable readings while the wearer goes about their day. The system learns to do this individually for each wearer. The deep tissue functionality and twelve hour battery life mean that the wearable is suitable to monitor a variety of useful physiological parameters, including cardiac output, blood pressure, and heart rate.

Ultrasound-enabled wearables are enjoying a moment, with a variety of these technologies emerging recently. These miniaturized, portable, and wearable versions of traditional bulky and stationary ultrasound equipment may be a game changer in terms of increasing the utility and application of ultrasound. This latest technology provides deep tissue imaging, further expanding the scope of what is possible with wearable ultrasound, and uses machine learning to refine the signal in moving patients.

“This technology has lots of potential to save and improve lives,” said Muyang Lin, a researcher involved in the study. “The sensor can evaluate cardiovascular function in motion. Abnormal values of blood pressure and cardiac output, at rest or during exercise, are hallmarks of heart failure. For healthy populations, our device can measure cardiovascular responses to exercise in real time and thus provide insights into the actual workout intensity exerted by each person, which can guide the formulation of personalized training plans.”

The technology can monitor tissues as deep as 164 mm (6.5 inches) below the surface of the skin, and the device is flexible, allowing good skin contact and comfort during use. Interestingly, the researchers had originally intended to design a blood pressure monitor, but realized that the technology may have greater functionality.

“At the very beginning of this project, we aimed to build a wireless blood pressure sensor,” said Lin. “Later on, as we were making the circuit, designing the algorithm and collecting clinical insights, we figured that this system could measure many more critical physiological parameters than blood pressure, such as cardiac output, arterial stiffness, expiratory volume and more, all of which are essential parameters for daily health care or in-hospital monitoring.”