Non-intrusive quantification of performance and its relationship to mood
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The number of jobs that takes place entirely or partially in a computer is nowadays very significant. These workplaces, as many others, often offer the key ingredients for the emergence of stress and the performance drop of its long-term effects: long hours sitting, sustained cognitive effort, pressure from competitiveness, among others. This has a toll on productivity and work quality, with significant costs for both organizations and workers. Moreover, a tired workforce is generally more susceptible to negative feelings and mood, which results in a negative environment. This paper contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. We propose a framework that assesses worker performance and a case study in which this approach was validated. We also show the relationship between performance and mood.
KeywordsWorker performance Mood Statistical analysis Distributed intelligence
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT—Fundaçã para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of Davide Carneiro is supported by a Post-Doctoral Grant by FCT (SFRH/BPD/109070/2015).
Compliance with ethical standards
Conflict of interest
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Balkin TJ, Wesensten NJ (2011) Differentiation of sleepiness and mental fatigue effects. Balkin, Thomas J.; Wesensten, Nancy J. Ackerman, Phillip L. (eds) Cognitive fatigue: multidisciplinary perspectives on current research and future applications. American Psychological Association, Washington, DC, pp 47–66. doi: 10.1037/12343-002
- Carneiro D, Novais P (2014) New applications of ambient intelligence. In: Ambient intelligence-software and applications, Advances in Intelligent Systems and Computing, vol. 291. Springer, pp. 225–232Google Scholar
- Kobayashi H, Demura S (2006) Relationships between chronic fatigue, subjective symptoms of fatigue, life stressors and lifestyle in Japanese high school students. School Health 2:5Google Scholar
- McNair DM, Lorr M, Droppleman LF (1971) Manual for the profile of mood states. Educational and Industrial Testing Service, San DiegoGoogle Scholar
- Newcombe PA, Boyle GJ (1995) High school students’ sports personalities: variations across participation level, gender, type of sport, and success. Int J Sport Psychol 26:277Google Scholar
- Perelli LP (1980) Fatigue stressors in simulated long-duration flight. Effects on performance, information processing, subjective fatigue, and physiological cost. Tech rep, DTIC documentGoogle Scholar
- Pimenta A, Carneiro D, Novais P, Neves J (2013) Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns. In: Hybrid artificial intelligent systems, Lecture Notes in Computer Science vol 8073, Springer, pp 222–231. doi: 10.1007/978-3-642-40846-5_23
- Pimenta A, Carneiro D, Novais P, Neves J (2015) Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers. In: Intelligent distributed computing VIII, Studies in Computational Intelligence vol 570. Springer, pp 29–39. doi: 10.1007/978-3-319-10422-5_5
- Samn SW, Perelli LP (1982) Estimating aircrew fatigue: a technique with application to airlift operations. Tech rep, DTIC documentGoogle Scholar