Predicting Daily Behavior via Wearable Sensors

Brian Clarkson, Alex Pentland


We report on ongoing research into how to statistically represent the experiences of a wearable computer user for the purposes of day-to-day behavior prediction. We combine natural sensor modalities (camera, microphone, gyros) with techniques for automatic labeling from sparsely labeled data. We have also taken the next required step to build robust statistical models by beginning an extensive data collection experiment, the “I Sensed” series, a 100 day data set consisting of full surround video, audio, and orientation.

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