A Feedback System for Assessing Human Condition Based on Keyboard Usage Activity
Abstract
This paper proposes a model for assessing a person’s emotional state based on their interaction with a computer keyboard. The model integrates a feedback mechanism, which significantly improves the accuracy of predicting a person’s emotional states. Through systematic analysis of the dynamics of keyboard usage activity and observation of individual environmental factors, the proposed approach not only predicts the state of the keyboard user but also adapts to changes in their behavior. The results of testing the model using specially developed software confirmed its effectiveness. Feedback-based systems improve human interaction with intelligent systems, contribute to the development of intelligent systems and humans. The obtained results confirm the necessity and direction for improving the human-machine interface and serve as a basis for their future integration into more complex systems for assessing the psycho-physiological state of a person.
References
Sharma, Aditi, Kapil Sharma, and Akshi Kumar. "Real-time emotional health detection using fine-tuned transfer networks with multimodal fusion."Neural computing and applications 35.31 (2023): 22935-22948.
Lee, Eunjung, et al. "A design platform for emotion-aware user interfaces."Proceedings of the 2014 Workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems. 2014.
Boyd, K L, and N Andalibi. "Automated emotion recognition in the workplace: How proposed technologies reveal potential futures of work."Proceedings of the ACM on human-computer interaction 7.CSCW1 (2023): 1-37.
Friesen, E, and P Ekman. "Facial action coding system: a technique for the measurement of facialmovement."Palo Alto 3.2 (1978): 5.
Zhao, G., and M. Pietikainen. ¨ "Dynamic texture recognition using local binary patterns with an application to facial expressions."IEEE Transactions on Pattern Analysis and Machine Intelligence 29.6 (2007): 915-928.
Vinciarelli, A., M. Pantic, and H. Bourlard. "Social signal processing: Survey of an emerging domain."Image and Vision Computing 27.12 (2009): 1743-1759.
Picard, Rosalind W. "Affective computing."MIT press, 2000.
Epp, Clayton, Michael Lippold, and Regan L. Mandryk. "Identifying emotional states using keystroke dynamics."Proceedings of the sigchi conference on human factors in computing systems. 2011.
Khanna, P., and M. Sasikumar. "Recognising emotions from keyboard stroke pattern."International journal of computer applications 11.9 (2010): 1-5.
Fairclough, S. H. "Fundamentals of physiological computing."Interacting with Computers 21.1-2 (2009): 133-145.
Zimmermann, P., S. Guttormsen, B. Danuser, and P. Gomez. "Affective computing - a rationale for measuring mood with mouse and keyboard."International Journal of Occupational Safety and Ergonomics 9.4 (2003): 539-551.
Li, J., K. Cheng, S. Wang, et al. "Feature selection: A data perspective."ACM Computing Surveys (CSUR) 50.6 (2017): 1-45.
Hou, Z., Q. Hu, and W. L. Nowinski. "On minimum variance thresholding."Pattern Recognition Letters 27.14 (2006): 1732-1743.
Granitto, P. M., C. Furlanello, F. Biasioli, et al. "Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products."Chemometrics and Intelligent Laboratory Systems 83.2 (2006): 83-90.
OpenWeatherMap. "API documentation."Retrieved from https://openweathermap.org/api.
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