Abstract
Productivity always was, and still is, the main goal of organizations, that being economic, governmental, military or educational. Having the means to control, detect and monitor features that have impact on productivity is a major issue, and subject to various investigation. Considering that most of the times, if not always, unconscious actions play a very important role in the way we work, study, socialize, and even in the way we have fun, the high significance of those factors becomes very clear. Monitoring unconscious actions, selecting those of them that do play a role regarding productivity, and trying to proactively take measures to improve processes, is then the goal of this work. Specifically, we are concerned about using computers peripherals to non-intrusively monitor user’s actions. The term non-intrusively assumes greater importance, as we are concerned with unconscious actions, thus we need to strongly ensure that no entropy is derived by the way this process is done. Peripherals such as mouse, keyboard, touch screens, and possibly webcams and microphones can act as sensors, completely hidden from the user. As we use them daily, they somehow assume part of our life, and can be used to collect data that will be processed to get useful information regarding that particular user. We then can build a behavioral profile, for instance, that will provide a better insight of user’s actions. We can predict some possibly negative features, such as stress, fatigue, level of attention, for instance. If detected or predicted, they can greatly help to better manage all the information we need, in the right way. We can suggest that someone takes a coffee break, because she/he is stressed. We can tell him/her to work/study in the morning, because the information we have collected suggests that is the period of the day that is more suitable to get better results, for that person. We can suggest postponing the following meeting, because the actual mood indicates that that person is more suitable to conflicts at that moment. In short, we aim to use computer peripherals and smartphones to collect data from the user, non-intrusively, aiming to detect or predict behavioral features (stress, fatigue, attention) and unconscious actions that will allow us to build a behavioral profile about the user, thus making it possible to improve productivity for instance. This is accomplished by monitoring mouse, keyboard and touch screen usage, non-intrusively, and in real time. Through the collected data, some inferences are made regarding the patterns of interaction that make it possible to detect variations regarding behavioral features and unconscious actions. With this information, we can have a detailed insight into those behavioral features, allowing us to proactively mitigate some of the potential problems that could arise. In this work a framework is proposed as a way to integrate all these features. We aim mainly to apply these concepts in learning contexts, as to improve student’s outcomes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pilsung, K., Sungzoon, C.: Keystroke Dynamics-Based User Authentication Using Long and Free Text Strings from Various Input Devices, pp. 72–93 (2015)
Rodrigues, M., Gonçalves, S., Carneiro, D., Novais, P., Fdez-Riverola, F.: Keystrokes and clicks: Measuring stress on E-learning students, management intelligent systems. In: Casillas, J., Martínez-López, F.J., Vicari, R., De la Prieta, F. (eds.) Second International Symposium, vol. 220, pp. 119–126. Springer - Series Advances in Intelligent and Soft Computing (ISBN 978-3-319-00568-3) (2013). http://dx.doi.org/10.1007/978-3-319-00569-0_15
Pimenta, A., et al.: Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers. In: Intelligent Distributed Computing VIII, pp. 29–39. Springer International Publishing (2015)
Gonçalves, S., Rodrigues, M., Carneiro, D., Fdez-Riverola, F., Novais, P.: Boosting Learning: Non-intrusive monitoring of student’s efficiency. Methodologies Intell. Syst. Technol. Enhanced Learn. Adv. Intell. Syst. Comput. 374, 73–80 (2015)
Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: The design, implementation and evaluation of the CenceMe application. In: Proceedings of SenSys (2008)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., et al.: The mobile sensing platform: An embedded activity recognition system. Pervasive Comput. IEEE 7(2), 32–41 (2008)
Kołakowska, A.: A review of emotion recognition methods based on keystroke dynamics and mouse movements. In: Proceedings of the 6th International Conference on Human System Interaction, Gdańsk (2013). https://doi.org/10.1109/hsi.2013.6577879
Wang, R., et al.: StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM
Ioannou, S.V., Raouzaiou, A.T., Tzouvaras, V.A., Mailis, T.P., Karpouzis, K.C., Kollias, S.D.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18(4), 423–435 (2005). https://doi.org/10.1145/1980022.1980177
Schuller, B., Lang, M., Rigoll, G.: Multimodal emotion recognition in audiovisual communication. In: Proceedings of the IEEE International Conference on Multimedia and Expo, ICME, Lausanne (2002). https://doi.org/10.1109/ICME.2002.1035889
Gill, A.J., French, R.M., Gergle, D., Oberlander, J.: Identifying emotional characteristics from short blog texts. In: Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 2237–2242 (2008)
Szwoch, W.: Using physiological signals for emotion recognition. In: Proceedings of the 6th International Conference on Human System Interaction, Gdańsk (2013). https://doi.org/10.1109/hsi.2013.6577880
Landowska, A.: Emotion monitor – concept, construction and lessons learned. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 75–80 (2015). https://doi.org/10.15439/2015f384
Van der Veen, J.S., Van der Waaij, B., Meijer, R.J.: Sensor data storage performance: SQL or NoSQL, physical or virtual. In: IEEE 5th International Conference on Cloud Computing (CLOUD) (2012)
Kang, Y.-S., et al.: MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sens. J. 16(2), 485–497 (2016)
Vieira, A.: Predicting online user behaviour using deep learning algorithms. arXiv preprint (2015). arXiv:1511.06247
A.N.University: Extracting User Profile with Deep Learning Techniques (2017). https://rsise.anu.edu.au/projects/pid/0000001123
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rodrigues, M., Santos, R., Novais, P. (2019). Our Actions, Ourselves: How Unconscious Actions Become a Productivity Indicator. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_74
Download citation
DOI: https://doi.org/10.1007/978-3-030-01057-7_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01056-0
Online ISBN: 978-3-030-01057-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)