Paper-based AI health sensor

Paper-based AI health sensor

Technology News |
By Nick Flaherty

Cette publication existe aussi en Français

Researchers in Japan have developed a disposable, wearable paper-based AI sensor for health monitoring.

A team at Tokyo University of Science (TUS) have developed the flexible paper-based AI health sensor composed of nanocellulose and zinc oxide (ZnO) nanoparticles that takes an optical input in real-time.

Achieving AI-based health monitoring and biological diagnosis requires a standalone sensor that operates independently without the need for constant connection to a central server. At the same time, the sensor must have a low power consumption for prolonged use, should be capable of handling the rapidly changing biological signals for real-time monitoring, be flexible enough to attach comfortably to the human body, and be easy to make and dispose of for hygiene reasons.

The researchers fabricated a photo-electronic artificial synapse device composed of gold electrodes on top of a 10 µm transparent film consisting of zinc oxide (ZnO) nanoparticles and cellulose nanofibres (CNFs).

The transparent film serves three main purposes. Firstly, it allows light to pass through, enabling it to handle optical input signals representing various biological information. Secondly, the cellulose nanofibres impart flexibility and can be easily disposed of by incineration. Thirdly, the ZnO nanoparticles are photoresponsive and generate a photocurrent when exposed to pulsed UV light and a constant voltage. This photocurrent mimics the responses transmitted by synapsis in the human brain, enabling the device to interpret and process information received from optical sensors.

The film was able to distinguish 4-bit input optical pulses and generate distinct currents in response to time-series optical input, with a rapid response time on the order of sub-seconds. This quick response is crucial for detecting sudden changes or abnormalities in health-related signals. Furthermore, when exposed to two successive light pulses, the electrical current response was stronger for the second pulse.

The device exhibits synaptic photocurrent in response to optical input and was tested with classification and time-series forecasting tasks. The memory capacity of short-term memory task, indicating the device’s ability to store past information, is 1.8 and the device can recognize handwritten digits with an accuracy of 88%.

The accuracy of handwritten digit recognition under bending was assessed with bending radii ranging from 9.5 mm to 16 mm over 1000 cycles and this does not affect its accuracy. It also burns in a few seconds, much like regular office paper, demonstrating its disposability.

“A paper-based optoelectronic synaptic device composed of nanocellulose and ZnO was developed for realizing physical reservoir computing. This device exhibits synaptic behavior and cognitive tasks at a suitable timescale for health monitoring,” says Associate Professor Takashi Ikuno.

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