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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 (ID 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&#x0025; energy w.r.t. a &#x201C;stand-alone&#x201D; 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.

Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs