Author ORCID Identifier

Perez - https://orcid.org/0000-0002-2852-2041

Document Type

Article

Publication Date

7-5-2011

Publication Title

Journal of Pervasive and Mobile Computing

Volume

8

Issue

5

First Page

717

Last Page

729

Abstract

This paper presents Centinela, a system that combines acceleration data with vital signs to achieve highly accurate activity recognition. Centinela recognizes five activities: walking, running, sitting, ascending, and descending. The system includes a portable and unobtrusive real-time data collection platform, which only requires a single sensing device and a mobile phone. To extract features, both statistical and structural detectors are applied, and two new features are proposed to discriminate among activities during periods of vital sign stabilization. After evaluating eight different classifiers and three different time window sizes, our results show that Centinela achieves up to 95.7% overall accuracy, which is higher than current approaches under similar conditions. Our results also indicate that vital signs are useful to discriminate between certain activities. Indeed, Centinela achieves 100% accuracy for activities such as running and sitting, and slightly improves the classification accuracy for ascending compared to the cases that utilize acceleration data only.

Comments

This is an Accepted Manuscript of an article published by Elsevier in Journal of Pervasive and Mobile Computing on July 5, 2011, available online:https://doi.org/10.1016/j.pmcj.2011.06.004

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