Paper Info
Title
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
Abstract
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 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.
Year
DOI
Venue
2012
10.1007/978-3-642-35395-6_30
IWAAL
Keywords
Field
DocType
traditional svm,human activity recognition,daily activity monitoring,ambient intelligence,multiclass hardware-friendly support vector,activity-based computing,computing power,computational cost reduction,computational cost,vector machine,continuous monitoring,exogenous computing resource,svm,activity recognition
Activity recognition,Computer science,Ambient intelligence,Support vector machine,Exploit,Continuous monitoring,Artificial intelligence,Inertial measurement unit,Machine learning,Cost reduction,Multiclass classification
Conference
Citations 
PageRank 
References 
164
5.75
13
Authors
5
Search Limit
100164
Name
Order
Citations
PageRank
Davide Anguita1100170.58
Alessandro Ghio266735.71
Luca Oneto383063.22
Xavier Parra452923.12
Jorge L. Reyes-Ortiz51645.75