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Training computationally efficient smartphone–based human activity recognition models
Anguita, Davide; Ghio, Alessandro; Oneto, Luca; Llanas Parra, Francesc Xavier; Reyes Ortiz, Jorge Luis
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. CETpD - Centre d’Estudis Tecnològics per a l’Atenció a la Dependència i la Vida Autònoma
The exploitation of smartphones for Human Activity Recognition (HAR) has been an active research area in which the development of fast and efficient Machine Learning approaches is crucial for preserving battery life and reducing computational requirements. In this work, we present a HAR system which incorporates smartphone-embedded inertial sensors and uses Support Vector Machines (SVM) for the classification of Activities of Daily Living (ADL). By exploiting a publicly available benchmark HAR dataset, we show the benefits of adding smartphones gyroscope signals into the recognition system against the traditional accelerometer-based approach, and explore two feature selection mechanisms for allowing a radically faster recognition: the utilization of exclusively time domain features and the adaptation of the L1 SVM model which performs comparably to non-linear approaches while neglecting a large number of non-informative features.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
Àrees temàtiques de la UPC::Informàtica
Human computer interaction
Smartphones
Human activity recognition
SVM
L1 SVM
Feature selection
Interacció persona-ordinador
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
         

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