Human Activity Recognition in WSN: A Comparative Study

DOI

10.2991/ijndc.2014.2.4.3How to use a DOI?

Keywords

Activity recognition; classification algorithm; data feature; smartphone position; ubiquitous computing

Abstract

Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including; classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components; a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Muhammad Arshad Awan
AU  - Zheng Guangbin
AU  - Cheong-Ghil Kim
AU  - Shin-Dug Kim
PY  - 2014
DA  - 2014/10/31
TI  - Human Activity Recognition in WSN: A Comparative Study
JO  - International Journal of Networked and Distributed Computing
SP  - 221
EP  - 230
VL  - 2
IS  - 4
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2014.2.4.3
DO  - 10.2991/ijndc.2014.2.4.3
ID  - Awan2014
ER  -