Non-intrusive Personalized Mental Workload Evaluation for Exercise Intensity Measure

Authors:
N. Luke Thomas;Yingzi Du;Tron Artavatkun;Jin-Hua She
Affiliations:
Purdue School of Engineering and Technology at Indianapolis, Electrical and Computer Engineering, Indianapolis, USA SL160;Purdue School of Engineering and Technology at Indianapolis, Electrical and Computer Engineering, Indianapolis, USA SL160;Purdue School of Engineering and Technology at Indianapolis, Electrical and Computer Engineering, Indianapolis, USA SL160;Tokyo University of Technology, Tokyo, Japan 192-0982
Venue:
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Year:
2009

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Abstract

Non-intrusive measures of mental workload signals are desirable, because they minimize artificially introduced noise, and can be more accurate. A new approach for non-intrusive personalized mental workload evaluation is presented. Our research results show that human mental workload is unique to each person, non-stationary, and not zero-state.