Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs. (arXiv:2206.07652v1 [eess.SP])

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 stage trees