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.
Article PDF
Avoid common mistakes on your manuscript.
References
D. J. Cook, J. C. Augusto and V. R. Jakkula, Ambient intelligence: Technologies, applications, and opportunities, Pervasive and Mobile Computing 5 (2009) 277–298.
A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu and P. Havinga, Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey, in ARCS Workshops (VDE Verlag, 2010), pp. 167–176.
L. S. Kmiecik, Cloud Centered, Smartphone Based Long-term Human Activity Recognition Solution, A Project Report, June 2013.
WEKA (Waikato Environment for Knowledge Analysis), http://www.cs.waikato.ac.nz/ml/weka/.
D. Guan, T. Ma, W. Yuan, Y. K. Lee and A. M. J. Sarkar, Review of Sensor-based Activity Recognition Systems, IETE Technical Review 28 (2011).
R. Jafari, W. Li, R. Bajcsy, S. Glaser and S. Sastry, Physical Activity Monitoring for Assisted Living at Home, in BSN, (2007), pp. 213–219.
D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell and B. G. Celler, Implementation of a Real-Time Human Movement Classifier Using a Tri-axial Accelerometer for Ambulatory Monitoring, IEEE Transactions on Information Technology in Biomedicine, 10 (2006) 156–167.
A. K. Bourke, J. V. O’Brien and G. M. Lyons, Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm, Gait & Posture, 26 (2007) 194–199.
O. D. Lara and M. A. Labrador, A Survey on Human Activity Recognition using Wearable Sensors, IEEE Communications Surveys & Tutorials, 15 (2013) 1192–1209.
S. Kaghyan, H. Sarukhanyan and D. Akopian, Human Movement Classification Approcahes that use Wearbale Sensors and Mobile Devices, SPIE-IS&T 8667 (2013).
A. M. Khan, M. H. Siddiqi and S. W. Lee, Exploratory Data Analysis of Acceleration Signals to Select LightWeight and Accurate Features for Real-Time Activity Recognition on Smartphones, Sensors 13 (2013).
M. A. Awan, Z. Guangbin S. D. and Kim, A Dynamic Approach to Recognize Activities in WSN, International Journal of Distributed Sensor Networks 2013 (2013).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Awan, M.A., Guangbin, Z., Kim, CG. et al. Human Activity Recognition in WSN: A Comparative Study. Int J Netw Distrib Comput 2, 221–230 (2014). https://doi.org/10.2991/ijndc.2014.2.4.3
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijndc.2014.2.4.3