Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks. (arXiv:2209.00839v1 [cs.LG])

Human Activity Recognition (HAR) based on inertial data is an increasingly
diffused task on embedded devices, from smartphones to ultra low-power sensors.
Due to the high computational complexity of deep learning models, most embedded
HAR systems are based on simple and not-so-accurate classic machine learning
algorithms. This work bridges the gap between on-device HAR and deep learning,
proposing a set of efficient one-dimensional Convolutional Neural Networks
(CNNs) deployable on general purpose microcontrollers (MCUs). Our CNNs are
obtained combining hyper-parameters optimization …

arxiv human microcontrollers networks neural networks