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Training Activity Recognition Models

by Martin Makaryan
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Researchers at Oxford University have created a new dataset that contains roughly 3,900 hours of data on human activity that researchers collected through wrist-worn accelerometers, wearable cameras, and sleep diaries from 151 people. The dataset can help train more accurate models that enable wrist-worn devices to recognize and interpret data on various activities, such as running or biking, and improves on conventional datasets that typically do not include such mixed and nuanced movements.

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Image credit: Luke Chesser

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