  Recent advances in both machine learning and Internet-of-Things have
attracted attention to automatic Activity Recognition, where users wear a
device with sensors and their outputs are mapped to a predefined set of
activities. However, few studies have considered the balance between wearable
power consumption and activity recognition accuracy. This is particularly
important when part of the computational load happens on the wearable device.
In this paper, we present a new methodology to perform feature selection on the
device based on Reinforcement Learning (RL) to find the optimum balance between
power consumption and accuracy. To accelerate the learning speed, we extend the
RL algorithm to address multiple sources of feedback, and use them to tailor
the policy in conjunction with estimating the feedback accuracy. We evaluated
our system on the SPHERE challenge dataset, a publicly available research
dataset. The results show that our proposed method achieves a good trade-off
between wearable power consumption and activity recognition accuracy.
