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Sensors 2016, 16(10), 1586; doi:10.3390/s16101586

Inferring Human Activity Recognition with Ambient Sound on Wireless Sensor Nodes

1
Ambient Intelligence Group, Saxion University of Applied Science, P.O. Box 70000, 7500 KB Enschede, The Netherlands
2
Pervasive Systems Group, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 10 May 2016 / Revised: 19 September 2016 / Accepted: 20 September 2016 / Published: 27 September 2016
(This article belongs to the Section Physical Sensors)

Abstract

A wireless sensor network that consists of nodes with a sound sensor can be used to obtain context awareness in home environments. However, the limited processing power of wireless nodes offers a challenge when extracting features from the signal, and subsequently, classifying the source. Although multiple papers can be found on different methods of sound classification, none of these are aimed at limited hardware or take the efficiency of the algorithms into account. In this paper, we compare and evaluate several classification methods on a real sensor platform using different feature types and classifiers, in order to find an approach that results in a good classifier that can run on limited hardware. To be as realistic as possible, we trained our classifiers using sound waves from many different sources. We conclude that despite the fact that the classifiers are often of low quality due to the highly restricted hardware resources, sufficient performance can be achieved when (1) the window length for our classifiers is increased, and (2) if we apply a two-step approach that uses a refined classification after a global classification has been performed. View Full-Text
Keywords: wireless sensor networks; sound; context awareness; feature extraction wireless sensor networks; sound; context awareness; feature extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Salomons, E.L.; Havinga, P.J.M.; van Leeuwen, H. Inferring Human Activity Recognition with Ambient Sound on Wireless Sensor Nodes. Sensors 2016, 16, 1586.

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