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Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload
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Author(s): Zhiming Wu (College of Computer Science, Sichuan University, Chengdu, China), Tao Lin (College of Computer Science, Sichuan University, Chengdu, China)and Ningjiu Tang (College of Computer Science, Sichuan University, Chengdu, China)
Copyright: 2020
Pages: 17
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch072
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Abstract
Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.
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