Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic

Davide Anguita (Università degli Studi di Genova, Italy)

Alessandro Ghio (Università degli Studi di Genova, Italy)

Luca Oneto (Università degli Studi di Genova, Italy)

Xavier Parra (Universitat Politècnica de Catalunya, Spain)

Jorge L. Reyes-Ortiz (Universitat Politècnica de Catalunya, Spain)

Abstract: In this paper we propose a novel energy efficient approach for the recog-nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabledand the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre-serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experimentsshow comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of theproposed method.

Keywords: SVM, activity recognition, assisted healthcare, energy efficiency, fixed-point arithmetic, remote monitoring, smartphones

Categories: D.2, H.1.2, I.2, I.2.6, I.2.9, J.3, J.7