research-article

An unobtrusive Android person verification using accelerometer based gait

Authors:

Chiung Ching Ho,

June-Yee LeowAuthors Info & Claims

MoMM '12: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia

Pages 271 - 274

https://doi.org/10.1145/2428955.2429007

Published: 03 December 2012 Publication History

Abstract

Mobile devices are ubiquitous enough to be considered as a valid token for proof of claim to identity. Unfortunately, the small size of mobile devices also means that it is prone to being misplaced and stolen. Gait biometric on accelerometer equipped mobile devices can add another layer of security in the event of theft, as it would refute a fraudulent claim to identity. As an extra bonus, gait data can be collected without the knowledge of the person handling the mobile device. In this paper, we describe how mobile devices can be used unobtrusively for the purpose of person verification. Person verification is achieved by classification of accelerometer based gait data recorded by said mobile device. Use cases for this approach is presented in a framework; supported by proof of concept experimental results.

References

[1]

Chan, H. K. Y., Zheng, H., Wang, H., Gawley, R., and Sterritt, R. Feasibility study on iPhone accelerometer for gait detection. Proceedings of the 5th International Conference on Pervasive Technologies for Healthcare, (2011).

[2]

Cunado, D., Nixon, M. S., and Carter, J. N. Automatic extraction and description of human gait models for recognition purposes. Computer Vision and Image Understanding 90, 1 (2003), 1--41.

Digital Library

[3]

Derawi, M. O., Bours, P., and Holien, K. Improved Cycle Detection for Accelerometer Based Gait Authentication. IEEE (2010), 312--317.

Digital Library

[4]

Derawi, M. O., Nickel, C., Bours, P., and Busch, C. Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition. 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), (2010), 306--311.

Digital Library

[5]

Gafurov, D., Helkala, K., and Sondrol, T. Biometric Gait Authentication Using Accelerometer Sensor. Journal of Computers 1, 7 (2006), 51--59.

[6]

Gafurov, D., Snekkenes, E., and Bours, P. Gait Authentication and Identification Using Wearable Accelerometer Sensor. IEEE (2007), 220--225.

[7]

Gafurov, D. Emerging Biometric Modalities: Challenges and Opportunities. In T. Kim, W. Fang, M. K. Khan, K. P. Arnett, H. Kang and D. Slezak, eds., Security Technology, Disaster Recovery and Business Continuity. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, 29--38.

[8]

Hausdorff, J. M., Peng, C. K., Ladin, Z., Wei, J. Y., and Goldberger, A. L. Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Journal of Applied Physiology (Bethesda, Md.: 1985) 78, 1 (1995), 349--358.

[9]

Hynes, M., Han Wang, and Kilmartin, L. Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms. IEEE (2009), 5187--5190.

[10]

Mayagoitia, R. E., Nene, A. V., and Veltink, P. H. Accelerometer and rategyroscopemeasurement of kinematics: an inexpensive alternative to opticalmotionanalysissystems. Journal of Biomechanics 35, 5 (2001), 537--542.

[11]

Moe-Nilssen, R. and Helbostad, J. L. Estimation of gait cycle characteristics by trunk accelerometry. Journal of Biomechanics 37, 1 (2004), 121--126.

[12]

Murray, M. P. Gait as a total pattern of movement. American Journal of Physical Medicine 46, 1 (1967), 290--333.

[13]

Nickel, C., Busch, C., Rangarajan, S., and Mobius, M. Using Hidden Markov Models for accelerometer-based biometric gait recognition. IEEE (2011), 58--63.

[14]

Nishiguchi, S., Yamada, M., Nagai, K., et al. Reliability and Validity of Gait Analysis by Android-Based Smartphone. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association, (2012).

[15]

Rogers, R. Android application development: programming with the Google SDK. O'Reilly, Sebastopol, 2009.

Digital Library

[16]

Terada, S., Enomoto, Y., Hanawa, D., and Oguchi, K. Gait Authentication using a wearable sensor. IEEE (2011), 1--3.

[17]

Terada, S., Enomoto, Y., Hanawa, D., and Oguchi, K. Performance of gait authentication using an acceleration sensor. IEEE (2011), 34--36.

[18]

Torrealba, R. R., Castellano, J. M., Fernandez-Lopez, G., and Grieco, J. C. Characterisation of gait cycle from accelerometerdata. Electronic Letters 43, 20 (2007).

[19]

BYOD: 'the inmates of the asylum have control'. Network World, 2012. http://www.networkworld.com/news/2012/041712-byod-258264.html.

[20]

Sensor | Android Developers. http://developer.android.com/reference/android/hardware/Sensor.html.

[21]

SensorEvent | Android Developers. http://developer.android.com/reference/android/hardware/SensorEvent.html.

[22]

Sixty-Two Percent of Organizations Allow Employees To Use Personal Mobile Devices For Work. Create Your Next Customer. http://createyournextcustomer.techweb.com/2012/05/22/sixty-two-percent-of-organizations-allow-employees-to-use-personal-mobile-devices-for-work/.

[23]

Federal Government Allowing More Personal Mobile Devices: Study - Government IT - News & Reviews. eWeek.com. http://www.eweek.com/c/a/Government-IT/Federal-Government-Allowing-More-Personal-Mobile-Devices-Study-790129/.

Cited By

Delgado Rodriguez SPrange SMecke LAlt FCiolfi LHogan TDöring TJenkins Tvan Dijk JHuron SLi ZCoyle DSigner B(2024)Act2Auth – A Novel Authentication Concept based on Embedded Tangible Interaction at DesksProceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3623509.3633360(1-15)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3623509.3633360
Ying JZhu TLiu QXiong CWeng ZChen TFu LLv MWu HWang TChen Y(2024)TrapCog: An Anti-Noise, Transferable, and Privacy-Preserving Real-Time Mobile User Authentication System With High AccuracyIEEE Transactions on Mobile Computing10.1109/TMC.2023.326507123:4(2832-2848)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3265071
Weng ZWu SWang QZhu T(2023)Enhancing Sensor-Based Mobile User Authentication in a Complex Environment by Deep LearningMathematics10.3390/math1117370811:17(3708)Online publication date: 29-Aug-2023
https://doi.org/10.3390/math11173708
Show More Cited By

Recommendations

BreathPrint: Breathing Acoustics-based User Authentication

MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

We propose BreathPrint, a new behavioural biometric signature based on audio features derived from an individual's commonplace breathing gestures. Specifically, BreathPrint uses the audio signatures associated with the three individual gestures: sniff, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MoMM '12: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia

December 2012

323 pages

ISBN:9781450313070

DOI:10.1145/2428955

Conference Chair:
Matthias Steinbauer
Johannes Kepler University Linz, Austria
,
Editor:
Ismail Khalil
Johannes Kepler University Linz, Austria
,
General Chair:
Eric Pardede
La Trobe University, Australia
,
Program Chair:
David Taniar
Monash University, Australia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

@WAS: International Organization of Information Integration and Web-based Applications and Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MoMM '12

Sponsor:

@WAS

MoMM '12: The 10th International Conference on Advances in Mobile Computing & Multimedia

December 3 - 5, 2012

Bali, Indonesia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
319
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Delgado Rodriguez SPrange SMecke LAlt FCiolfi LHogan TDöring TJenkins Tvan Dijk JHuron SLi ZCoyle DSigner B(2024)Act2Auth – A Novel Authentication Concept based on Embedded Tangible Interaction at DesksProceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3623509.3633360(1-15)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3623509.3633360
Ying JZhu TLiu QXiong CWeng ZChen TFu LLv MWu HWang TChen Y(2024)TrapCog: An Anti-Noise, Transferable, and Privacy-Preserving Real-Time Mobile User Authentication System With High AccuracyIEEE Transactions on Mobile Computing10.1109/TMC.2023.326507123:4(2832-2848)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3265071
Weng ZWu SWang QZhu T(2023)Enhancing Sensor-Based Mobile User Authentication in a Complex Environment by Deep LearningMathematics10.3390/math1117370811:17(3708)Online publication date: 29-Aug-2023
https://doi.org/10.3390/math11173708
Zhu TWeng ZSong QChen YLiu QChen YLv MChen T(2022)EspialCog: General, Efficient and Robust Mobile User Implicit Authentication in Noisy EnvironmentIEEE Transactions on Mobile Computing10.1109/TMC.2020.301249121:2(555-572)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TMC.2020.3012491
Zhu TWeng ZChen GFu L(2020)A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion SensorsSensors10.3390/s2014387620:14(3876)Online publication date: 11-Jul-2020
https://doi.org/10.3390/s20143876
Zhu TQu ZXu HZhang JShao ZChen YPrabhakar SYang J(2020)RiskCog: Unobtrusive Real-Time User Authentication on Mobile Devices in the WildIEEE Transactions on Mobile Computing10.1109/TMC.2019.289244019:2(466-483)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TMC.2019.2892440
(2018)A Survey on Gait RecognitionACM Computing Surveys10.1145/323063351:5(1-35)Online publication date: 29-Aug-2018
https://dl.acm.org/doi/10.1145/3230633
Shukla DWei GXue DJin ZPhoha V(2018)Body-Taps: Authenticating Your Device Through Few Simple Taps2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)10.1109/BTAS.2018.8698602(1-8)Online publication date: Oct-2018
https://doi.org/10.1109/BTAS.2018.8698602
Blasco JChen TTapiador JPeris-Lopez P(2016)A Survey of Wearable Biometric Recognition SystemsACM Computing Surveys10.1145/296821549:3(1-35)Online publication date: 16-Sep-2016
https://dl.acm.org/doi/10.1145/2968215
(2016)Different strokes for different folks? Revealing the physical characteristics of smartphone users from their swipe gesturesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2016.01.00188:C(51-61)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.ijhcs.2016.01.001
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten