Web: http://arxiv.org/abs/2206.07652

June 16, 2022, 1:11 a.m. | Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino

cs.LG updates on arXiv.org arxiv.org

Human Activity Recognition (HAR) has become an increasingly popular task for
embedded devices such as smartwatches. Most HAR systems for ultra-low power
devices are based on classic Machine Learning (ML) models, whereas Deep
Learning (DL), although reaching state-of-the-art accuracy, is less popular due
to its high energy consumption, which poses a significant challenge for
battery-operated and resource-constrained devices. In this work, we bridge the
gap between on-device HAR and DL thanks to a hierarchical architecture composed
of a decision tree …

arxiv cnns decision human microcontrollers on stage trees

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY