Next Article in Journal
Architecture of a Service-Enabled Sensing Platform for the Environment
Previous Article in Journal
Correction: Tang, C. Y. and Chen, X.Y. A Class of Coning Algorithms Based on a Half-Compressed Structure. Sensors 2014, 14, 14289–14301
Article Menu

Export Article

Open AccessReview
Sensors 2015, 15(2), 4430-4469; doi:10.3390/s150204430

Mining Personal Data Using Smartphones and Wearable Devices: A Survey

Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Received: 5 December 2014 / Accepted: 9 February 2015 / Published: 13 February 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1009 KB, uploaded 16 February 2015]   |  

Abstract

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices. View Full-Text
Keywords: data mining; mobile computing; personal data; wearable computing data mining; mobile computing; personal data; wearable computing
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Rehman, M.H.; Liew, C.S.; Wah, T.Y.; Shuja, J.; Daghighi, B. Mining Personal Data Using Smartphones and Wearable Devices: A Survey. Sensors 2015, 15, 4430-4469.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top