  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 (DT) and a one dimensional Convolutional Neural Network (1D
CNN). The two classifiers operate in a cascaded fashion on two different
sub-tasks: the DT classifies only the easiest activities, while the CNN deals
with more complex ones. With experiments on a state-of-the-art dataset and
targeting a single-core RISC-V MCU, we show that this approach allows to save
up to 67.7% energy w.r.t. a "stand-alone" DL architecture at iso-accuracy.
Additionally, the two-stage system either introduces a negligible memory
overhead (up to 200 B) or on the contrary, reduces the total memory occupation.
