International Journal of Networked and Distributed Computing

Volume 2, Issue 4, November 2014, Pages 221 - 230

Human Activity Recognition in WSN: A Comparative Study

Authors
Muhammad Arshad Awan, Zheng Guangbin, Cheong-Ghil Kim, Shin-Dug Kim
Corresponding Author
Muhammad Arshad Awan
Available Online 15 October 2017.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
2 - 4
Pages
221 - 230
Publication Date
2017/10
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
https://doi.org/10.2991/ijndc.2014.2.4.3How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Muhammad Arshad Awan
AU  - Zheng Guangbin
AU  - Cheong-Ghil Kim
AU  - Shin-Dug Kim
PY  - 2017
DA  - 2017/10
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  - https://doi.org/10.2991/ijndc.2014.2.4.3
ID  - Awan2017
ER  -