Título:
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Human activity recognition on smartphones for mobile context awareness
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Autor/a:
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Anguita, Davide; Ghio, Alessandro; Oneto, Luca; Llanas Parra, Francesc Xavier; Reyes Ortiz, Jorge Luis
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Otros autores:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement |
Abstract:
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Activity-Based Computing [1] 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. In addition to the clear computational advantages
of fixed-point arithmetic, it is easy to show the regularization effect of the
number of bits and then the connections with the Statistical Learning Theory. 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. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors -Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Cognitive environments -Human-centered computing -Telèfons intel·ligents -- Aplicacions -Reconeixement de formes (Informàtica) -Computació centrada en humans -Entorns cognitius |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
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