alexa Grip Kinetic Profile Variability in Adult Signature Writing | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Grip Kinetic Profile Variability in Adult Signature Writing

Bassma Ghali1,2, Khondaker Mamun1,2and Tom Chau1,2*

1Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Road, Toronto, Ontario, Canada

2Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto,Ontario, Canada

*Corresponding Author:
Tom Chau
Institute of Biomaterials and Biomedical Engineering
University of Toronto, 164 College Street
Toronto, Ontario, Canada
Tel: +1 416 425 6220 (ext 3515)
Fax: +1 416 425 1634
E-mail: [email protected]

Received Date: August 05, 2013; Accepted Date: September 16, 2013; Published Date: September 21, 2013

Citation: Ghali B, Mamun K, Chau T (2013) Grip Kinetic Profile Variability in Adult Signature Writing. J Biomet Biostat 4: 174 doi: 10.4172/2155-6180.1000174

Copyright: © 2013 Ghali B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Previous studies of handwriting grip kinetics have demonstrated the ability to classify writers based on the topography of grip forces associated with signature writing. However, the topographic representation requires a large array of individual sensors in practice. The possibility of differentiating participants on the basis of a summative, temporal force profile is yet unknown. In this study, we investigated the variability of features derived from a time-evolving total grip force profile. Using an instrumented writing utensil, twenty adult participants provided 600 samples of a well-practised bogus signature over a period of 10 days. Deploying a combination of temporal, spectral and information-theoretic features, a linear discriminant analysis classifier outperformed nonparametric and nonlinear classifier alternatives and discriminated among participants with an average misclassification rate of 5.8% as estimated by cross-validation. These results suggest the existence of a unique kinetic profile for each writer even when generating the same written product. Our findings highlight the potential of using grip kinetics as a biometric measure.


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