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Analysis and Monitoring of Mental Fatigue
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Analysis and Monitoring of Mental Fatigue

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  • 1. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Analysis and Monitoring of Mental Fatigue Andr´e Pimenta Ribeiro Supervisor: Paulo Novais University of Minho Department of Informatics July 7, 2013 1/22
  • 2. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work 1 Introduction 2 Fatigue 3 AmI 4 Fatigue Detection 5 Study Performed 6 Monitoring System 7 Conclusions and Future Work 2/22
  • 3. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Motivation The Problem Fatigue is considered one of the main causes for human failure and low performance. It is also considered a key factor towards health and wellness 3/22
  • 4. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Application Contexts Learning, Work, Productivity, Wellness and Health. 4/22
  • 5. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Objectives Main purpose A system capable of detecting and monitoring mental fatigue through the analysis of keyboard and mouse Interaction. 1 Analysis of patterns of behavior and human activities 2 Development of a capture module 3 Collect data on user fatigue behavior 4 Analysis of the fatigue patterns 5 Validation of behavior patterns and models 6 Specification of an alert and recommendation system 5/22
  • 6. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Fatigue Fatigue Lack of energy, physical exertion, physical discomfort, lack of motivation, and sleepiness Mental fatigue: difficulties with concentration, attention, visual perception, and somnolence Physical fatigue: loss of muscle strength, speed or agility, thus limiting the performance of physical tasks 6/22
  • 7. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Ambient Intelligence A computer is seen as a proactive tool capable of autonomously adapting to the tasks of the day-to-day life of its user. Sensing Acting Human-Computer Interaction Privacy and security challenges 7/22
  • 8. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Fatigue Detection fatigue symptoms ...A combination of symptoms that include low performance (loss of attention, slow reaction, poor performance in tasks where they have skills) and subjective feelings of sleepiness and tiredness... 8/22
  • 9. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Behavioral Biometrics Behavioral Biometrics, keystroke dynamics and mouse dynamics. 9/22
  • 10. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Methodology Collect data on user normal behavior Collect data on user fatigued behavior Using the concept of acute fatigue Statistical value with Mann–Whitney test Select the best metrics 10/22
  • 11. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Case study 20 volunteers Data collected at the beginning and end of job Natural use of the computer Selected 5 of 14 metric under study: Mouse Acceleration Mouse Velocity Time Between Keys Key Time Press Error per Key 11/22
  • 12. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Mouse Acceleration State Average Standard D. Median Max Min Normal 0.423 0.103 0.409 0.617 0.242 Fatigue 0.394 0.092 0.405 0.546 0.208 12/22
  • 13. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Mouse Velocity State Average Standard D. Median Max Min Normal 0.500 0.132 0.484 0.702 0.262 Fatigue 0.462 0.119 0.469 0.618 0.226 13/22
  • 14. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Time Between Keys State Average Standard D. Median Max Min Normal 79.826 7.752 80.500 88.240 63.480 Fatigue 85.530 5.870 87.290 92.050 72.700 14/22
  • 15. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Key Time Press State Average Standard D. Median Max Min Normal 469.193 399.321 299.726 1316.930 78.059 Fatigue 956.367 632.898 943.678 2156.400 87.892 15/22
  • 16. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Error per Key State Average Standard D. Median Max Min Normal 7.643 2.768 7.444 13.137 4.625 Fatigue 9.010 2.600 8.597 13.217 4.942 16/22
  • 17. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Classifying Fatigue K-nearest neighbors algorithm WEKA - weka.classifiers.lazy.ibk @attributes (mouse acceleration, mouse velocity, time between keys, key press time, error per key) A model based on instances of different users A model based on instances of a single user 17/22
  • 18. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Monitoring System Prototype developed 18/22
  • 19. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Conclusions It is possible to detect and monitor fatigue through KeyStroke Dynamics and Mouse Dynamics Key press time, Time between keys, Mouse Acceleration and Mouse Velocity are good metrics Fatigue is manifested by the loss of performance and increased errors It is possible to detect acute fatigue with technological devices 19/22
  • 20. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Future Work Consider new metrics Introduction of external factors in the system, such as sleepiness, stress and mood states in order to improve the system Detection through new devices Validation through specialists in health and psychology 20/22
  • 21. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Relevant Work Carneiro D., Novais P., Catal˜ao F., Marques J., Pimenta A., Neves J., Dynamically Improving Collective Environments through Mood Induction Procedures, Ambient Intelligence- Software and Applications – 4th International Symposium on Ambient Intelligence (ISAmI 2013), Ad van Berlo, Kasper Hallenborg, Juan M. Corchado, Dante I. Tapia, Paulo Novais (eds), Springer - Series Advances in Intelligent and Soft Computing, Vol 219, ISBN 978-3-319-00565-2, pp 33-40, 2013. Pimenta A., Carneiro D., Novais P. and Neves J., Monitoring Mental Fatigue through the analysis of Keyboard and Mouse Interaction, HAIS 2013 - 8th International Conference on Hybrid Artificial Intelligence Systems Patterns, Salamanca, Spain, to appear in LNAI Serie Springer 2013. 21/22
  • 22. Introduction Fatigue AmI Fatigue Detection Study Performed Monitoring System Conclusions and Future Work Analysis and Monitoring of Mental Fatigue Andr´e Pimenta Ribeiro Supervisor: Paulo Novais University of Minho Department of Informatics July 7, 2013 22/22