  The vital statistics of the last century highlight a sharp increment of the
average age of the world population with a consequent growth of the number of
older people. Service robotics applications have the potentiality to provide
systems and tools to support the autonomous and self-sufficient older adults in
their houses in everyday life, thereby avoiding the task of monitoring them
with third parties. In this context, we propose a cost-effective modular
solution to detect and follow a person in an indoor, domestic environment. We
exploited the latest advancements in deep learning optimization techniques, and
we compared different neural network accelerators to provide a robust and
flexible person-following system at the edge. Our proposed cost-effective and
power-efficient solution is fully-integrable with pre-existing navigation
stacks and creates the foundations for the development of fully-autonomous and
self-contained service robotics applications.
