JUCS - Journal of Universal Computer Science 19(9): 1295-1314, doi: 10.3217/jucs-019-09-1295
Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic
expand article infoDavide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra§, Jorge L. Reyes-Ortiz§
‡ Università degli Studi di Genova, Genoa, Italy§ Universitat Politècnica de Catalunya, Vilanova i la Geltrú, Spain
Open Access
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.
activity recognition, remote monitoring, SVM, smartphones, energy efficiency, fixed-point arithmetic, assisted healthcare