  Activity recognition, as an important component of behavioral monitoring and
intervention, has attracted enormous attention, especially in Mobile Cloud
Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently
resource constrained wearable devices have been gaining popularity, their
battery life is limited and constrained by the frequent wireless transmission
of data to more computationally powerful back-ends. This paper proposes an
ultra-low power activity recognition system using a novel adaptive compressed
sensing technique that aims to minimize transmission costs. Coarse-grained
on-body sensor localization and unsupervised clustering modules are devised to
autonomously reconfigure the compressed sensing module for further power
saving. We perform a thorough heuristic optimization using Grammatical
Evolution (GE) to ensure minimal computation overhead of the proposed
methodology. Our evaluation on a real-world dataset and a low power wearable
sensing node demonstrates that our approach can reduce the energy consumption
of the wireless data transmission up to $81.2\%$ and $61.5\%$, with up to
$60.6\%$ and $35.0\%$ overall power savings in comparison with baseline and a
naive state-of-the-art approaches, respectively. These solutions lead to an
average activity recognition accuracy of $89.0\%$ -- only $4.8\%$ less than the
baseline accuracy -- while having a negligible energy overhead of on-node
computation.
