  Human Activity Recognition (HAR) is a relevant inference task in many mobile
applications. State-of-the-art HAR at the edge is typically achieved with
lightweight machine learning models such as decision trees and Random Forests
(RFs), whereas deep learning is less common due to its high computational
complexity. In this work, we propose a novel implementation of HAR based on
deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting
low-power general purpose processors with a RISC-V instruction set. BNNs yield
very small memory footprints and low inference complexity, thanks to the
replacement of arithmetic operations with bit-wise ones. However, existing BNN
implementations on general purpose processors impose constraints tailored to
complex computer vision tasks, which result in over-parametrized models for
simpler problems like HAR. Therefore, we also introduce a new BNN inference
library, which targets ultra-compact models explicitly. With experiments on a
single-core RISC-V processor, we show that BNNs trained on two HAR datasets
obtain higher classification accuracy compared to a state-of-the-art baseline
based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with
either less memory (up to 91%) or more energy-efficiency (up to 70%), depending
on the complexity of the features extracted by the RF.
