Developed by a team at the University of Chicago’s Pritzker School of Molecular Engineering (PME), the device aims to change the way health data is processed. Their findings are published in Matter.
“With this work we’ve bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyse health data right on our own bodies,” said Sihong Wang, a materials scientist and Assistant Professor of Molecular Engineering.
Acquiring a detailed profile an individual’s health requires a visit to a hospital or doctor’s surgery. In the future, Wang said in a statement, people’s health could be tracked continuously by wearable electronics that can detect disease before symptoms appear. Unobtrusive, wearable computing devices are one step toward making this vision a reality, the team said.
The future of healthcare that Wang foresees includes wearable biosensors to track complex indicators of health including levels of oxygen, sugar, metabolites and immune molecules in people’s blood. One of the keys to making these sensors feasible is their ability to conform to the skin.
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As such skin-like wearable biosensors emerge and begin collecting more and more information in real-time, the analysis becomes exponentially more complex as a single piece of data must be put into the broader perspective of a patient’s history and other health parameters.
Current smart phones are not capable of the kind of complex analysis required to learn a patient’s baseline health measurements and pick out important signals of disease, but artificial intelligence platforms that integrate machine learning to identify patterns in extremely complex datasets can do a better job. According to Wang, sending information from a device to a centralised AI location is not ideal.
“Sending health data wirelessly is slow and presents a number of privacy concerns,” he said. “It is also incredibly energy inefficient; the more data we start collecting, the more energy these transmissions will start using.”
Wang’s team set out to design a chip that could collect data from multiple biosensors and draw conclusions about a person’s health using machine learning approaches. Importantly, they wanted it to be wearable on the body and integrate seamlessly with skin.
“With a smart watch, there’s always a gap,” said Wang. “We wanted something that can achieve very intimate contact and accommodate the movement of skin.”
Wang and his colleagues turned to polymers, which can be used to build semiconductors and electrochemical transistors but also stretch and bend. They assembled polymers into a device that allowed the artificial-intelligence-based analysis of health data. Rather than work like a typical computer, the neuromorphic computing chip functions more like a human brain, storing and analysing data in an integrated way.
To test the utility of their new device, Wang’s group used it to analyse electrocardiogram (ECG) data, training the device to classify ECGs into five categories: healthy or four types of abnormal signals. Then, they tested it on new ECGs. Whether or not the chip was stretched or bent, they showed, it could accurately classify the heartbeats.
More work is needed to test the power of the device in deducing patterns of health and disease, but could be used either to send patients or clinicians alerts, or to automatically tweak medications.
“If you can get real-time information on blood pressure, for instance, this device could very intelligently make decisions about when to adjust the patient’s blood pressure medication levels,” said Wang. That kind of automatic feedback loop is already used by some implantable insulin pumps, he added.
He is planning new iterations of the device to expand the type of devices with which it can integrate and the types of machine learning algorithms it uses.
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