Abstract
Recent advances in embedded technology have enabled
more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition
(EAR), where users wearing a device such as a smartphone
or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has
mainly focused on improving accuracy by using resource intensive techniques such as multi-stream deep networks. Although this approach has provided state-of-the-art results,
in most cases it neglects the natural resource constraints
(e.g. battery) of wearable devices. We develop a Reinforcement Learning model-free method to learn energy-aware
policies that maximize the use of low-energy cost predictors while keeping competitive accuracy levels. Our results
show that a policy trained on an egocentric dataset is able
use the synergy between motion and vision sensors to effectively tradeoff energy expenditure and accuracy on smartglasses operating in realistic, real-world conditions