Volume 2, Issue 4, November 2014, Pages 221 - 230
Human Activity Recognition in WSN: A Comparative Study
- Muhammad Arshad Awan, Zheng Guangbin, Cheong-Ghil Kim, Shin-Dug Kim
- Corresponding Author
- Muhammad Arshad Awan
Available Online 15 October 2017.
- https://doi.org/10.2991/ijndc.2014.2.4.3How to use a DOI?
- Activity recognition; classification algorithm; data feature; smartphone position; ubiquitous computing
- 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.
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 -