The University of California San Diego researchers have made a giant leap forward in wireless ultrasound monitoring for subjects in motion, by developing the first fully integrated autonomous wearable ultrasound system for deep-tissue monitoring.
Traditional soft ultrasonic sensors all require tethering cables for data and power transmission, which largely constrains the user's mobility. In contrast, the team's ultrasonic system-on-patch (USoP), can continuously measure central blood pressure, heart rate, cardiac output, and other physiological signals of users on the go, for up to twelve hours at a time.
"This project gives a complete solution to wearable ultrasound technology—not only the wearable sensor, but also the control electronics are made in wearable form factors," said Muyang Lin, the first author of the study. "We made a truly wearable device that can sense deep-tissue vital signs wirelessly."
According to the lab's findings, the system-on-patch can detect physiological signals from tissues as deep as 164mm. To do so, it relies on a small, flexible control circuit that communicates with an ultrasound transducer array to collect and transmit data wirelessly. A machine learning component then helps interpret the data and track subjects in motion.
"This technology has lots of potential to save and improve lives," Lin said. "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 personalised training plans."
The USoP also represents a breakthrough in the development of the Internet of Medical Things (IoMT), a term for a network of medical devices connected to the internet, wirelessly transmitting physiological signals into the cloud for computing, analysis and professional diagnosis.
While developing its latest innovation, the team was surprised to discover that it had more capabilities than initially anticipated.
"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."
One of the main challenges the team faced was making sure that the measurements were not affected by the relative movement between the wearable ultrasonic sensor and the tissue target, which happens when the users are in motion.
In order to address those, the researchers developed a machine learning model that relied on an advanced adaptation algorithm. The algorithm was then able to automatically analyse the received signals and choose the most appropriate channel to keep track of the moving target, while taking into account the domain distribution discrepancies between different subjects.
"We can train the algorithm on one subject and apply it to many other new subjects with minimal retraining," said Ziyang Zhang, co-first author of the paper
Moving forward, the sensor will be tested among larger populations.
"So far, we have only validated the device performance on a small but diverse population," said Xiaoxiang, co-first author of the study. "As we envision this device as the next generation of deep-tissue monitoring devices, clinical trials are our next step."
The researcher's findings were published in the journal Nature Biotechnology.
