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Here’s how to use health detecting feature in smartwatches

In order to detect potential emergent health concerns, researchers are developing skin-like electronics combined with Artificial Intelligence. This study was published in the journal Matter with the title ‘Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence”.

Precision medical sensors that are placed on the skin to do health monitoring and diagnostics could be made possible by this technology in the coming days. It’d be like having a cutting-edge medical institution at your disposal at all times. Such a skin-like device is being sophisticated in a project between the U.S Department of Energy’s Argonne National Laboratory and the University of Chicago’s Pritzker School of Molecular Engineering. Worn daily, future wearable electronics could potentially detect possible emerging health problems like heart diseases, cancer or multiple sclerosis even before obvious symptoms appear.

The device could also perform a personalized analysis of the tracked health data while minimizing the need for its wireless transmission. According to the person’s age, medical history and other factors, the diagnosis for the same health measurements could vary. Such a diagnosis with health information being progressively gathered over an extended period, is very data intensive. Such a device would need to collect and process a huge amount of data, well above what even the best smartwatches can do today. And it would have to do this data crunching with very low power consumption in a very small space.

The team’s skin-like neuromorphic chip contains a thin film of a plastic semiconductor combined with stretchable gold nanowire electrodes. Even when stretched to twice its normal size, their device functioned as planned without the formation of any cracks. For one test, the team built an AI device and trained it to distinguish healthy electrocardiogram signals from 4 different signals indicating health problems. The device was more than 95% effective at correctly identifying the ECG signals, after training.

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