Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals

Physiological signals such as heart rate can provide valuable information about an individual’s state and activity. However, existing work on computer vision has not yet explored leveraging these signals to enhance egocentric video understanding. In this work, we propose a model for reasoning on multimodal data to jointly predict activities and energy expenditures. We use heart rate signals as privileged self-supervision to derive energy expenditure in a training stage. A multitask objective is used to jointly optimize the two tasks. Additionally, we introduce a dataset that contains 31 hours of egocentric video augmented with heart rate and acceleration signals. This study can lead to new applications such as a visual calorie counter.

Presented at:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, July 21-26, 2017

 Record created 2017-08-21, last modified 2018-01-28

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