Practical Insights on Incremental Learning of New Human Physical
  Activity on the Edge
George Arvanitakis Jingwei Zuo Mthandazo Ndhlovu Hakim Hacid
Edge Machine Learning (Edge ML), which shifts computational intelligence from
cloud-based systems to edge devices, is attracting significant interest due to
its evident benefits including reduced latency, enhanced data privacy, and
decreased connectivity reliance. While these advantages are compelling, they
introduce unique challenges absent in traditional cloud-based approaches. In
this paper, we delve into the intricacies of Edge-based learning, examining the
interdependencies among: (i) constrained data storage on Edge devices, (ii)
limited computational power for training, and (iii) the number of learning
classes. Through experiments conducted using our MAGNETO system, that focused
on learning human activities via data collected from mobile sensors, we
highlight these challenges and offer valuable perspectives on Edge ML.