Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency
Mobile devices are resource-limited systems that provide a large number of services and features. Smartphones, for example, implement advanced functionalities and services for the final user, in addition to conventional communication capabilities. Machine Learning algorithms can help in providing such advanced functionalities, but mobile systems suffer from issues related to their resource-limited nature such as, for example, limited battery capacity and processing power and, therefore, even simple pattern recognition activities can become too demanding, in this respect. We propose here a method to design a Human Activity Recognition algorithm, which takes into account the fact that only limited resources are available for its execution. In particular, we restrict the hypothesis space of possible recognition models by applying some advanced concepts from Statistical Learning Theory, so as to force the selection of models with good generalization ability but low computational complexity. Then, the learned model can be effectively implemented on a mobile and resource-limited device: the experiments, carried out on a current-generation smartphone, show the benefits of the proposed approach in terms of both model accuracy and battery duration.
mobile devices energy efficiency local rademacher complexity supervised learning performance assessment