Paper Reading #31: Identifying Emotional States using Keystroke Dynamics


Reference
Authors : Clayton Epp, Michael Lippold, and Regan L. Mandryk
Affiliations: Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
Presentation:CHI 2011, May 7–12, 2011, Vancouver, BC, Canada.

Summary
Hypothesis

Given the two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment, the authors present a solution to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard.


Contents

There have been various methods of evaluating emotional activities that have seen varying rates of success, but they still exhibit one or both of two main problems preventing wide- scale use: they can be intrusive to the user, and can require specialized equipment that is expensive and not found in typical home or office environments. This system using key strokes is more intuitive, unobtrusive and has a wider range of users.

Methods

The authors conducted a field study that gathered keystrokes as users performed their daily computer tasks. Using an experience-sampling approach, users labeled the data with their level of agreement with 15 emotional states and provided additional keystrokes by typing fixed pieces of text.

From the raw keystroke data, the authors extracted a number of features derived mainly from key duration (dwell time) and key latency (flight time). We then created decision-tree classifiers for 15 emotional states, using the derived feature set.

Results

Using the extracted keystroke features, the authors created classifiers for 15 emotional states.

Keystroke dynamics can accurately classify at least two levels of seven emotional states (confidence, hesitance, nervousness, relaxation, sadness, and tired ).

The top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition,they were able to show promise for anger and excitement, with accuracies of 84%.


Discussion

The paper does a pretty good job in studying the emotional states using basic equipment of everyday life; keyboard. Unlike its predecessors, it is unobtrusive, easily available and has a larger user base. These qualities of this system makes it really easy for anyone to integrate emotional factors while designing a computer program, or a technical device. Systems can be designed that can analyze the emotional states of people and perform accordingly so that an agitated user does not get more agitated, an angry user does not show his anger to the application and do other mistakes, and things like that. It seems like it should be an integral part of all the technologies using keyboards.

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