Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI. ; Peer Reviewed ; Postprint (author's final draft)
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
2012-01-01
Miscellaneous
Electronic Resource
English
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial , Computació centrada en humans , Activity recognition , Smartphones , Reconeixement de formes (Informàtica) , Human activity recognition , Hardware-Friendly , Telèfons intel·ligents -- Aplicacions , Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes , SVM , Human-centered computing
DDC: | 629 |
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