  The use of wearable and mobile devices for health monitoring and activity
recognition applications is increasing rapidly. These devices need to maximize
their accuracy and active time under a tight energy budget imposed by battery
and small form-factor constraints. This paper considers energy harvesting
devices that run on a limited energy budget to recognize user activities over a
given period. We propose a technique to co-optimize the accuracy and active
time by utilizing multiple design points with different energy-accuracy
trade-offs. The proposed technique switches between these design points at
runtime to maximize a generalized objective function under tight harvested
energy budget constraints. We evaluate the proposed approach experimentally
using a custom hardware prototype and fourteen user studies. The proposed
approach achieves both 46% higher expected accuracy and 66% longer active time
compared to the highest performance design point.
