But, sending the data from an individual device to a centralised AI location isn’t efficient enough and is energy-intensive
Incorporating wearable technology with artificial intelligence, researchers from the University of Chicago, have designed a stretchy and flexible device that captures data on heat and then processes it by imitating the human brain. Today, a wide range of fitness bands that wearables and other health-related devices are available in the marketplace. However, they do not allow for sophisticated analysis of a health of the patient’s baseline measurements, and can’t detect indicators of illness.
That’s where the capabilities for Artificial Intelligence could be employed to make a difference. machine learning can assist in identifying patterns in data sets that are complex. However, sending data from the device to an centralised AI place isn’t effective enough and is energy-intensive.
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In the new research, the team set out at creating a chip which could not only gather information from multiple biosensors, but also make a decision regarding the health of a person through AI. With the smartwatch there’s always an unfinished. The goal was to create a device that could create a very intimate connection and allow for the movement of the skin, said Sihong Wang Materials scientist and Associate Professor in Molecular Engineering. Wang is one of the authors of the study , which was published in Matter.
The team has decided to use polymers which can be used to create electrochemical transistors as well as semiconductors. They are also very elastic and flexible. They’ve incorporated the polymers in a device that could process the data using AI. The chip, dubbed neuromorphic computing, functions more or less like a computer, and more like the human brain. This is because it’s capable of storing and process data in a way that is integrated.
Researchers have also examined the efficacy of the device, and utilized it to analyze the electrocardiogram (ECG) data as well as the heart’s electrical activity. The device was trained to categorize these data in four kinds and discovered it gave precise information about whether the chip was bent or not.