Abstract
Knowledge workers can benefit from tools to support them in performing deep, concentrated work. Research in biofeedback has shown success in training relaxation, but not in directly influencing task performance. One reason for this may be the difficulties users have in contextualizing biofeedback signals for different task situations. This presents an opportunity to leverage the strengths of case-based reasoning to select the feedback mechanism that will produce the best response. This paper describes initial research into the Adaptive Choice Case-Based Reasoning (ACCBR) system, that learns from and interacts with a user to assist them in achieving greater concentration and productivity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Craig Roth. 2019: When we exceeded 1 billion knowledge workers, December 11 2019. https://blogs.gartner.com/craig-roth/2019/12/11/2019-exceeded-1-billion-knowledge-workers/
Csikszentmihalyi, M., Larson, R.: Flow and The Foundations of Positive Psychology. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-017-9088-8
Allen, D.: Getting Things Done. Penguin, New York (2003)
Marzbani, H., Marateb, H.R., Mansourian, M.: Neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin. Neurosci. 7(2), 143–158 (2016)
Thaler, R.H., Sunstein, C.R.: Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin, New York (2009)
Weekes, T.R., Eskridge, T.C.: A neurofeedback-driven humanoid to support deep work. In: Proceedings of the 33rd Florida Conference on Recent Advances in Robotics, 14–16 May 2020
De Houwer, J., Hermans, D.: Cognition and Emotion: Reviews of Current Research and Theories. Psychology Press, New York (2010)
Plechawska-Wójcik, M., Tokovarov, M., Kaczorowska, M., Zapała, D.: A three-class classification of cognitive workload based on EEG spectral data. Appl. Sci. 9(24), 5340 (2019)
Thejaswini, S., Ravikumar, K.M., Jhenkar, L., Aditya, N., Abhay, K.K.: Analysis of EEG based emotion detection of DEAP and SEED-IV databases using SVM. Int. J. Recent Technol. Eng 8, 207–211 (2019)
Cai, H., Zhang, X., Zhang, Y., Wang, Z., Hu, B.: A case-based reasoning model for depression based on three-electrode EEG data. IEEE Trans. Affect. Comput. 11(3), 383–392 (2020)
Pandey, B., Kundra, D.: Diagnosis of EEG-based diseases using data mining and case-based reasoning. Int. J. Intell. Syst. Des. Comput. 1(1/2), 43 (2017)
Ram, A., Santamaría, J.C.: Continuous case-based reasoning. Artif. Intell. 90(1–2), 25–77 (1997)
Weekes, T., Eskridge, T.C: Nudging into flow: optimizing productivity with a choice architecture. In: Cognitive Economics Workshop, London, UK, November 7–8 2019. Cognitive Economics Society (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Eskridge, T.C., Weekes, T.R. (2020). Opportunities for Case-Based Reasoning in Personal Flow and Productivity Management. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-58342-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58341-5
Online ISBN: 978-3-030-58342-2
eBook Packages: Computer ScienceComputer Science (R0)