research-article

Gait-based authentication using a wrist-worn device

Authors:

Guglielmo Cola,

Marco Avvenuti,

Alessio VecchioAuthors Info & Claims

MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 208 - 217

https://doi.org/10.1145/2994374.2994393

Published: 28 November 2016 Publication History

Abstract

Every individual has a distinctive way of walking. For this reason gait can be a key element of biometric techniques aimed at authenticating and/or identifying the user of a wearable device. This paper presents a lightweight method that uses the acceleration collected at the user's wrist for authentication purposes. The user's typical gait pattern is learned during the first period of use, then detection of anomalies in a set of acceleration-based features is used to understand if a new user, a possible impostor or a thief, is wearing the device. The method has been successfully evaluated with 15 volunteers, showing an Equal Error Rate of 2.9%. These results suggest that gait-based authentication with a wrist-worn device can be carried out with high accuracy levels. A comparison with a similar method executed on a pocket-worn device is also included.

References

[1]

S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and A. Vecchio. A smartphone-based fall detection system. Pervasive and Mobile Computing, 8(6):883--899, 2012.

Digital Library

[2]

H. J. Ailisto, M. Lindholm, J. Mantyjarvi, E. Vildjiounaite, and S.-M. Makela. Identifying people from gait pattern with accelerometers. In Proceedings of SPIE, volume 5779, pages 7--14, 2005.

[3]

N. Alshurafa, J.-A. Eastwood, M. Pourhomayoun, S. Nyamathi, L. Bao, B. Mortazavi, and M. Sarrafzadeh. Anti-cheating: Detecting self-inflicted and impersonator cheaters for remote health monitoring systems with wearable sensors. In Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pages 92--97, June 2014.

Digital Library

[4]

L. Bianchi, D. Angelini, and F. Lacquaniti. Individual characteristics of human walking mechanics. Pflügers Archiv, 436(3):343--356, 1998.

[5]

V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):15, 2009.

Digital Library

[6]

G. Cola, M. Avvenuti, A. Vecchio, G. Z. Yang, and B. Lo. An on-node processing approach for anomaly detection in gait. IEEE Sensors Journal, 15(11):6640--6649, Nov 2015.

[7]

G. Cola, M. Avvenuti, A. Vecchio, G.-Z. Yang, and B. Lo. An unsupervised approach for gait-based authentication. In Proceedings of the 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pages 1--6. IEEE, June 2015.

[8]

G. Cola, A. Vecchio, and M. Avvenuti. Improving the performance of fall detection systems through walk recognition. Journal of Ambient Intelligence and Humanized Computing, 5(6):843--855, 2014.

[9]

M. Derawi, P. Bours, and K. Holien. Improved cycle detection for accelerometer based gait authentication. In Proceedings of the Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pages 312--317, Oct 2010.

Digital Library

[10]

T. Fawcett. An introduction to ROC analysis. Pattern recognition letters, 27(8):861--874, 2006.

Digital Library

[11]

D. Gafurov, E. Snekkenes, and P. Bours. Gait authentication and identification using wearable accelerometer sensor. In Proceedings of the IEEE Workshop on Automatic Identification Advanced Technologies, pages 220--225, June 2007.

[12]

D. Gafurov, E. Snekkenes, and P. Bours. Improved gait recognition performance using cycle matching. In Proceedings of the IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pages 836--841, April 2010.

Digital Library

[13]

M. A. Hall and G. Holmes. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15(6):1437--1447, 2003.

Digital Library

[14]

A. H. Johnston and G. M. Weiss. Smartwatch-based biometric gait recognition. In Proceedings of the IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pages 1--6, Sept 2015.

[15]

J. Kirby, C. Tibbins, C. Callens, B. Lang, M. Thorogood, W. Tigbe, and W. Robertson. Young people's views on accelerometer use in physical activity research: Findings from a user involvement investigation. ISRN Obesity, 2012, 2012.

[16]

J. Kwapisz, G. Weiss, and S. Moore. Cell phone-based biometric identification. In Proceedings of the Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pages 1--7. IEEE, Sept 2010.

[17]

B. Lo, S. Thiemjarus, A. Panousopoulou, and G.-Z. Yang. Bioinspired design for body sensor networks {life sciences}. IEEE Signal Processing Magazine, 30(1):165--170, 2013.

[18]

H. Lu, J. Huang, T. Saha, and L. Nachman. Unobtrusive gait verification for mobile phones. In Proceedings of the 2014 ACM International Symposium on Wearable Computers, pages 91--98, New York, NY, USA, 2014.

Digital Library

[19]

D. Mizell. Using gravity to estimate accelerometer orientation. In Proceedings of the IEEE International Symposium on Wearable Computing, pages 252--253, 2003.

Digital Library

[20]

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

[21]

T. T. Ngo, Y. Makihara, H. Nagahara, Y. Mukaigawa, and Y. Yagi. The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognition, 47(1):228--237, 2014.

Digital Library

[22]

S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers. A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 9(1), 2012.

[23]

Y. Ren, Y. Chen, M. C. Chuah, and J. Yang. User verification leveraging gait recognition for smartphone enabled mobile healthcare systems. IEEE Transactions on Mobile Computing, 14(9):1961--1974, Sept 2015.

Digital Library

[24]

L. Rong, Z. Jianzhong, L. Ming, and H. Xiangfeng. A wearable acceleration sensor system for gait recognition. In Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications, pages 2654--2659, May 2007.

[25]

Shimmer. http://www.shimmersensing.com, 2016.

Cited By

Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645091
Zouridakis PDinakarrao S(2024)Performance- and Energy-Aware Gait-Based User Authentication With Intermittent Computation for IoT DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.331309743:2(600-612)Online publication date: Feb-2024
https://doi.org/10.1109/TCAD.2023.3313097
Mawanda RKeishing SWang JMarchang J(2024)Secure by Design Smart Authentication for Care Robots to Support the Elderly2024 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT58233.2024.10541033(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.1109/ICIT58233.2024.10541033
Show More Cited By

Recommendations

Continuous authentication through gait analysis on a wrist-worn device
Abstract
Being distinctive of every individual, gait can be used as a biometric feature to authenticate the owner of a wearable device. This paper proposes and evaluates an authentication method that relies on the acceleration signal acquired ...
Biometric Gait Analysis Using Wrist-Mounted Wearable Sensors
Computer Information Systems and Industrial Management
Abstract
The paper presents a proposed gait biometrics system using wearable sensors. The work carried out verified the possibility of building systems using motion sensors located on the right and left wrist. The biometric system presented used input data ...
Portable Activity Monitoring System for Temporal Parameters of Gait Cycles

A portable and wireless activity monitoring system was developed for the estimation of temporal gait parameters. The new system was built using three-axis accelerometers to automatically detect walking steps with various walking speeds. The accuracy of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2016

307 pages

ISBN:9781450347501

DOI:10.1145/2994374

General Chairs:
Takahiro Hara
Osaka University, Japan
,
Hiroshi Shigeno
Keio University, Japan

Copyright © 2016 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]

In-Cooperation

EAI: The European Alliance for Innovation
SIGAPP: ACM Special Interest Group on Applied Computing
SIGBED: ACM Special Interest Group on Embedded Systems
KDDI R&D Laboratories Inc.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MOBIQUITOUS 2016

MOBIQUITOUS 2016: Computing, Networking and Services

November 28 - December 1, 2016

Hiroshima, Japan

Acceptance Rates

MOBIQUITOUS 2016 Paper Acceptance Rate 26 of 87 submissions, 30%;

Overall Acceptance Rate 26 of 87 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
490
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)3

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645091
Zouridakis PDinakarrao S(2024)Performance- and Energy-Aware Gait-Based User Authentication With Intermittent Computation for IoT DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.331309743:2(600-612)Online publication date: Feb-2024
https://doi.org/10.1109/TCAD.2023.3313097
Mawanda RKeishing SWang JMarchang J(2024)Secure by Design Smart Authentication for Care Robots to Support the Elderly2024 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT58233.2024.10541033(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.1109/ICIT58233.2024.10541033
Salvador-Ortega IVivaracho-Pascual CSimon-Hurtado A(2023)A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable DevicesSensors10.3390/s2303105423:3(1054)Online publication date: 17-Jan-2023
https://doi.org/10.3390/s23031054
Vhaduri SDibbo SCheung W(2023)Implicit IoT authentication using on-phone ANN models and breathing dataInternet of Things10.1016/j.iot.2023.10100324(101003)Online publication date: Dec-2023
https://doi.org/10.1016/j.iot.2023.101003
Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://doi.org/10.1007/s00521-023-08863-9
Wu YHassan MHu W(2022)SafeGaitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346076:2(1-27)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534607
Liu JSong WShen LHan JRen K(2022)Secure User Verification and Continuous Authentication Via Earphone IMUIEEE Transactions on Mobile Computing10.1109/TMC.2022.3193847(1-15)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3193847
Vecchio ANocerino RCola G(2022)Gait-based Authentication: Evaluation of Energy Consumption on Commercial Devices2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops53856.2022.9767367(793-798)Online publication date: 21-Mar-2022
https://doi.org/10.1109/PerComWorkshops53856.2022.9767367
Lee SLee SPark ELee JKim I(2022)Gait-Based Continuous Authentication Using a Novel Sensor Compensation Algorithm and Geometric Features Extracted From Wearable SensorsIEEE Access10.1109/ACCESS.2022.322181310(120122-120135)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3221813
Show More Cited By

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents