  Much of the energy consumption in buildings is due to HVAC systems, which has
motivated several recent studies on making these systems more energy-
efficient. Occupancy and activity are two important aspects, which need to be
correctly estimated for optimal HVAC control. However, state-of-the-art methods
to estimate occupancy and classify activity require infrastructure and/or
wearable sensors which suffers from lower acceptability due to higher cost.
Encouragingly, with the advancement of the smartphones, these are becoming more
achievable. Most of the existing occupancy estimation tech- niques have the
underlying assumption that the phone is always carried by its user. However,
phones are often left at desk while attending meeting or other events, which
generates estimation error for the existing phone based occupancy algorithms.
Similarly, in the recent days the emerging theory of Sparse Random Classifier
(SRC) has been applied for activity classification on smartphone, however,
there are rooms to improve the on-phone process- ing. We propose a novel sensor
fusion method which offers almost 100% accuracy for occupancy estimation. We
also propose an activity classifica- tion algorithm, which offers similar
accuracy as of the state-of-the-art SRC algorithms while offering 50% reduction
in processing.
