  Advances in embedded systems have enabled integration of many lightweight
sensory devices within our daily life. In particular, this trend has given rise
to continuous expansion of wearable sensors in a broad range of applications
from health and fitness monitoring to social networking and military
surveillance. Wearables leverage machine learning techniques to profile
behavioral routine of their end-users through activity recognition algorithms.
Current research assumes that such machine learning algorithms are trained
offline. In reality, however, wearables demand continuous reconfiguration of
their computational algorithms due to their highly dynamic operation.
Developing a personalized and adaptive machine learning model requires
real-time reconfiguration of the model. Due to stringent computation and memory
constraints of these embedded sensors, the training/re-training of the
computational algorithms need to be memory- and computation-efficient. In this
paper, we propose a framework, based on the notion of online learning, for
real-time and on-device machine learning training. We propose to transform the
activity recognition problem from a multi-class classification problem to a
hierarchical model of binary decisions using cascading online binary
classifiers. Our results, based on Pegasos online learning, demonstrate that
the proposed approach achieves 97% accuracy in detecting activities of varying
intensities using a limited memory while power usages of the system is reduced
by more than 40%.
