[R]esearchers in Canada are leveraging artificial intelligence to analyze food bite-by-bite. All diners have to do is record themselves eating. The research aims to improve on old–fashioned food journals and smartphone apps that estimate calories and nutrients based on photographs of plated meals or scans of bar codes on packaged foods.

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[Alexander Wong, a director of the Vision and Image Processing Lab at the University of Waterloo in Ontario, Canada] and his colleagues use video footage recorded with a cellphone to measure the amount of food on a spoon as it travels from dish to mouth. The algorithm doesn’t yet identify the type of food, but measuring portion size is a first step to deducing the calories and nutritional content of food as it’s eaten, without additional input from the diner.

“We put a camera on the table, and you forget about it,” says Yuhao Chen, a research assistant professor in the lab. The team is also analyzing still images of food and footage captured by wearable glasses.

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