Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones. An Application to Human Activity Recognition on Smartphones

Most state-of-the-art machine learning (ML) algorithms do not consider the computational constraints of implementing their learned models on mobile devices. These constraints are, for example, the limited depth of the arithmetic unit, the memory availability, and the battery capacity. We propose a new learning framework, which relies on Statistical Learning Theory that includes these constraints inside the learning process itself. This new framework allows to train advanced resource-sparing ML models and to efficiently deploy them on smart mobile devices. The advantages of our proposal are presented on a smartphone-based Human Activity Recognition application and compared against a conventional ML approach.