Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities

Smartphones emerge from the incorporation of new services and features into mobile phones, allowing to implement advanced functionalities for the final users. The implementation of Machine Learning (ML) algorithms on the smartphone itself, without resorting to remote computing systems, allow to achieve such goals without expensive data transmission. However, smartphones are resource-limited devices and, as such, suffer from many issues, which are typical of stand-alone devices, such as limited battery capacity and processing power. We show in this paper how to build a thrifty classifier by exploiting bit-based hypothesis spaces and local Rademacher Complexities. The resulting classifier is tested on a real-world Human Activity Recognition application, implemented on a Samsung Galaxy S II smartphone.