The Importance of Pen Motion Pattern Groups for Semi-Automatic Classification of Handwriting into Mental Workload Classes
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In this paper, we introduce the pen motion pattern groups (PMPGs) and their contribution to the classification of handwriting into cognitive mental workload classes. We demonstrate the importance of PMPGs by providing an efficient semi-automatic machine learning-based classification framework that distinguishes between handwritten texts written by the same person under different mental workloads. Our evaluation framework is non-language-dependent since we used stroke features, which are not language-specific, and it takes into account the variability in behavioral biometrics between different writers. The handwritten text data was collected using the Computerized Penmanship Evaluation Tool. This digitizer provided accurate temporal measures throughout the writing. As a first stage, the participants were asked to write a given text in the Hebrew language. Then, as a second stage, the participants’ cognitive workload was manipulated by asking them to hold a number in their memory during the entire writing task. In our experiments, we show that incorporating the PMPGs into the classification process yielded an average cognitive load discrimination accuracy of 92.16%, which decreased to 72.90% when the PMPGs were not considered. The separation of handwritten strokes into PMPGs allows us to account for the fact that the strokes are affected differently under different cognitive mental workloads. This novel distinction between PMPGs is important since the handwriting process in each PMPG is different from a perceptual motor and brain-hand control point of view. Moreover, most of the features that are influenced by cognitive workload are those that cannot be discerned by an expert when looking at a handwritten text on paper, such as azimuth, tilt, velocity, acceleration, and pressure.
KeywordsHandwriting Classification Computerized measures Mental workload Digitizer
Compliance with Ethical Standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee.
Conflict of Interest
The authors declare that they have no conflict of interest.
Informed consent was obtained from all individual participants included in the study.
- 3.Cambria E, Hussain A. 2012. Sentic computing: techniques, tools, and applications, vol 2. Springer Science & Business Media.Google Scholar
- 6.Faundez-Zanuy M, Sesa-Nogueras E, Roure-Alcobé J. On the relevance of age in handwritten biometric recognition. IEEE international carnahan conference on security technology (ICCST); 2012. p. 105–109.Google Scholar
- 7.Faundez-Zanuy M, Sesa-Nogueras E, Roure-Alcobé J, Esposito A, Mekyska J, López de Ipiña K. A preliminary study on aging examining online handwriting. 5th IEEE conference on cognitive infocommunications (CogInfoCom); 2014. p. 221–224.Google Scholar
- 11.Gomez-Barrero M, Galbally J, Fierrez J, Ortega-Garcia J, Plamondon R. Enhanced on-line signature verification based on skilled forgery detection using sigma-lognormal features. 2015 international conference on biometrics (ICB). IEEE; 2015. p. 501–506.Google Scholar
- 17.Kotsavasiloglou C, Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M. Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomed Signal Process Control 2016;31(1):174–80.Google Scholar
- 18.Kotsiantis SB. Supervised machine learning: a review of classification techniques. Proceedings of the 2007 conference on emerging artificial intelligence applications in computer engineering. Amsterdam, The Netherlands; 2007. p. 3–24.Google Scholar
- 20.Likforman-Sulem L, Esposito A, Faundez-Zanuy M, Clémençon S, Bassis S, Esposito A, Morabito CF. Extracting style and emotion from handwriting. Advances in neural networks: computational and theoretical issues; 2015. p. 347–355.Google Scholar
- 22.López-de Ipiña K, Alonso JB, Solé-Casals J, Barroso N, Henriquez P, Faundez-Zanuy M, Travieso CM, Ecay-Torres M, Martínez-Lage P, Eguiraun H. On automatic diagnosis of Alzheimer’s disease based on spontaneous speech analysis and emotional temperature. Cogn Comput 2015;7(1):44–55.CrossRefGoogle Scholar
- 37.Rosenblum S, Livneh-Zirinski M. Do relationships exist between brain-hand language and daily function characteristics of children with a hidden disability? Recent advances of neural network models and applications: proceedings of the 23rd workshop of the italian neural networks society (SIREN); 2014. p. 269–281.Google Scholar
- 45.Smekal Z, Mekyska J, Rektorova I, Faundez-Zanuy M. Analysis of neurological disorders based on digital processing of speech and handwritten text. International symposium on signals circuits and systems (ISSCS); 2013. p. 1–6.Google Scholar
- 49.Suzuki Y, Kazuo M, Jiro T. Stylus enhancement to enrich interaction with computers. Proceedings of the 12th international conference on human-computer interaction: interaction platforms and techniques, HCI’07; 2007. p. 133–142.Google Scholar
- 51.Taylor BT, Bove V M Jr. Graspables: Grasp-recognition as a user interface. Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’09; 2009. p. 917–926.Google Scholar
- 57.Xiaojun B, Tomer M, Gonzalo R, Ravin B, Ken H. An exploration of pen rolling for pen-based interaction. Proceedings of the 21st annual ACM symposium on user interface software and technology, UIST ’08; 2008. p. 191–200.Google Scholar