Next Article in Journal
Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
Next Article in Special Issue
Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks
Previous Article in Journal
A Microfluidic Approach for Inducing Cell Rotation by Means of Hydrodynamic Forces
Previous Article in Special Issue
Incentives for Delay-Constrained Data Query and Feedback in Mobile Opportunistic Crowdsensing
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1314; doi:10.3390/s16081314

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones

1
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China
2
School of Computer Science and Technology, Soochow University, Soochow 215000, China
3
School of Urban Rail Transportation, Soochow University, Soochow 215000, China
4
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210000, China
This paper is an extended version of our paper entitled “Recognizing the Operating Hand from Touchscreen Traces on Smartphones”. In the Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management (KSEM), Chongqing, China, 28–30 October 2015; pp. 199–211.
*
Author to whom correspondence should be addressed.
Academic Editor: Yu Wang
Received: 7 July 2016 / Revised: 5 August 2016 / Accepted: 10 August 2016 / Published: 20 August 2016

Abstract

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy. View Full-Text
Keywords: operating hand recognition; hand-changing process detection; user interface adjustment; smartphone; touchscreen; accelerometer and gyroscope; supervised classification operating hand recognition; hand-changing process detection; user interface adjustment; smartphone; touchscreen; accelerometer and gyroscope; supervised classification
Figures

Figure 1

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

Guo, H.; Huang, H.; Huang, L.; Sun, Y.-E. Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones. Sensors 2016, 16, 1314.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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