A New Automated Method for Evaluating Mental Workload Using Handwriting Features

• WU Zhiming

  College of Computer Science, Sichuan University

• XU Hongyan

  College of Computer Science, Sichuan University College of Tianfu, SouthWestern University of Finance and Economics

• LIN Tao

  College of Computer Science, Sichuan University

Abstract

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E100.D (9), 2147-2155, 2017

  The Institute of Electronics, Information and Communication Engineers

Keywords

• handwriting feature
• mental workload
• automated evaluation

Details

• CRID: 1390282679355460608
• NII Article ID: 130006038502
• DOI: 10.1587/transinf.2016edp7354
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E100.D/9/E100.D_2016EDP7354/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed