Analysis of Mental Fatigue and Mood States in Workplaces
Mental fatigue is a concern for a range of reasons, including its negative impact on productivity and quality of life in general. The maximal working capacity and performance of an individual, whether physical or mental, generally also decreases as the day progresses. The loss of these capabilities is associated with the emergence of fatigue, which is particularly visible in long and demanding tasks or repetitive jobs. However, good management of working time and of the effort invested in each task, as well as the effect of breaks at work can result in better performance and better mental health, delaying the effects of fatigue. In this paper a model and prototype are proposed to detect and monitor fatigue, based on behavioral biometrics (Keystroke Dynamics and Mouse Dynamics). Using this approach, the aim is to develop leisure and work context-aware environments that may improve quality of life and individual performance, as well as productivity in organizations.
KeywordsChronic Fatigue Syndrome Sleep Deprivation Mental Fatigue Mental Workload Fatigue Level
This work is part-funded by ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT—Fundao̧ão para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project Scope UID/CEC/00319/2013.
- 3.Balkin, T.J., Wesensten, N.J.: Differentiation of sleepiness and mental fatigue effects. (2011)Google Scholar
- 7.Kobayashi, H., Demura, S.: Relationships between chronic fatigue, subjective symptoms of fatigue, life stressors and lifestyle in Japanese high school students. Sch. Health 2, 5 (2006)Google Scholar
- 9.McNair, D.M., Lorr, M., Droppleman, L.F.: Manual for the Profile of Mood States. Educational and Industrial Testing Service, San Diego CA (1971)Google Scholar
- 10.Newcombe, P.A., Boyle, G.J.: High school students’ sports personalities: variations across participation level, gender, type of sport, and success. Int. J. Sport Psychol. 26, 277 (1995)Google Scholar
- 11.Perelli, L.P.: Fatigue stressors in simulated long-duration flight. Subjective Fatigue, and Physiological Cost. Technical Report, DTIC Document, Effects on Performance, Information Processing (1980)Google Scholar
- 12.Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns. In: Hybrid Artificial Intelligent Systems, pp. 222–231. Springer, Berlin (2013)Google Scholar
- 13.Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers. In: Intelligent Distributed Computing VIII, pp. 29–39. Springer, Berlin (2015)Google Scholar
- 14.Ramos, C., Augusto, J.C., Shapiro, D.: Ambient intelligence-the next step for artificial intelligence. IEEE Int. Syst. 23(2), 15–18 (2008)Google Scholar
- 15.Samn, S.W., Perelli, L.P.: Estimating aircrew fatigue: a technique with application to airlift operations. Technical Report, DTIC Document (1982)Google Scholar
- 17.Spielberger, C.D.: State-Trait Anxiety Inventory. John Wiley & Sons, Inc. (2010)Google Scholar