Paper Title: Identifying emotional states using keystroke dynamics
Authors: Clayton Epp, Michael Lippold and Regan Mandryk
Author Bios:
Clayton Epp: is currently a software engineer for a private consulting company and holds a master's degree in CHI from the University of Saskatchewan
Michael Lippold: is currently a masters student at the University of Saskatchewan
Regan Mandryk: is an Assistant Professor in the Interaction Lab in the Department of Computer Science at the University of Saskatchewan
Presentation Venue: CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems that took place at New York (ACM)
Summary:
Hypothesis: To investigate the efficacy of keystroke dynamics for determining emotional state, the authors conducted a field study that gathered keystrokes as users performed their daily computer tasks. The authors found a solution of detecting user's emotional states through their typing rhythms on the common computer keyboard.
Hypothesis: To investigate the efficacy of keystroke dynamics for determining emotional state, the authors conducted a field study that gathered keystrokes as users performed their daily computer tasks. The authors found a solution of detecting user's emotional states through their typing rhythms on the common computer keyboard.
How the hypothesis was tested: The authors' methodology consisted of two primary components: data collection process and data processing. The collection process consisted of gathering and labeling users' keystroke data. The data processing consisted of extracting relevant keystroke features to build classifiers.
The authors chose an experience-sampling methodology for two reasons: 1) they were interested in emotional data gathered in the real-world, rather than induced in a laboratory setting through emotion-elicitation methods. Our results are intended for use in real-world system, and gathering the data for modeling from naturally occuring emotion increases out ecological validity.
The data collection software was written in C# and used a low-level windows hook to scan each keystroke as it was entered by the user. This program ran in the background, gathering keystrokes regardless of the application that was currently in focus.
The authors chose an experience-sampling methodology for two reasons: 1) they were interested in emotional data gathered in the real-world, rather than induced in a laboratory setting through emotion-elicitation methods. Our results are intended for use in real-world system, and gathering the data for modeling from naturally occuring emotion increases out ecological validity.
The data collection software was written in C# and used a low-level windows hook to scan each keystroke as it was entered by the user. This program ran in the background, gathering keystrokes regardless of the application that was currently in focus.
Results: The researchers used undersampling on many of the models to help make the data more meaningful in terms of detectable levels of emotion. They found that two of their "tired" models performed most accurately with the most consistency, and that models utilizing the undersampling performed better overall.
Discussion:
Effectiveness: This paper was okay.The idea of being able to classify a use's emotion by keystrokes is not a very impressive one. However, I doubt the accuracy of such a system would be high because there are so many factors that go into classifying that. For e.g. I can type fast because I am angry, or just because i am drinking a Monster energy drink or may because i am in a hurry. This same rate of keystrokes could have 3 different kinds of emotions related to it. Looking at this, we can conclude that this system needs to have more features that can accurately determine the use emotions. Of course the system is effective when looked at the overall picture but due to the same reason it can have limited implementations and applications. Overall, the authors did achieve their goals!
Effectiveness: This paper was okay.The idea of being able to classify a use's emotion by keystrokes is not a very impressive one. However, I doubt the accuracy of such a system would be high because there are so many factors that go into classifying that. For e.g. I can type fast because I am angry, or just because i am drinking a Monster energy drink or may because i am in a hurry. This same rate of keystrokes could have 3 different kinds of emotions related to it. Looking at this, we can conclude that this system needs to have more features that can accurately determine the use emotions. Of course the system is effective when looked at the overall picture but due to the same reason it can have limited implementations and applications. Overall, the authors did achieve their goals!
No comments:
Post a Comment