Journal ArticleDOI
Automated stress detection using keystroke and linguistic features: An exploratory study
TLDR
Results show that it is possible to classify cognitive and physical stress conditions relative to non-stress conditions based on keystroke and linguistic features with accuracy rates comparable to those currently obtained using affective computing methods.Abstract:
Monitoring of cognitive and physical function is central to the care of people with or at risk for various health conditions, but existing solutions rely on intrusive methods that are inadequate for continuous tracking. Less intrusive techniques that facilitate more accurate and frequent monitoring of the status of cognitive or physical function become increasingly desirable as the population ages and lifespan increases. Since the number of seniors using computers continues to grow dramatically, a method that exploits normal daily computer interactions is attractive. This research explores the possibility of detecting cognitive and physical stress by monitoring keyboard interactions with the eventual goal of detecting acute or gradual changes in cognitive and physical function. Researchers have already attributed a certain amount of variability and ''drift'' in an individual's typing pattern to situational factors as well as stress, but this phenomenon has not been explored adequately. In an attempt to detect changes in typing associated with stress, this research analyzes keystroke and linguistic features of spontaneously generated text. Results show that it is possible to classify cognitive and physical stress conditions relative to non-stress conditions based on keystroke and linguistic features with accuracy rates comparable to those currently obtained using affective computing methods. The proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost. This research demonstrates the potential of exploiting continuous monitoring of keyboard interactions to support the early detection of changes in cognitive and physical function.read more
Citations
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Journal ArticleDOI
Objective measures, sensors and computational techniques for stress recognition and classification
Nandita Sharma,Tom Gedeon +1 more
TL;DR: This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress, and discusses non-invasive and unobtrusive sensors for measuring computed stress, a term coined in the paper.
Proceedings ArticleDOI
Identifying emotional states using keystroke dynamics
TL;DR: This work collected participants' keystrokes and their emotional states via self-reports, extracted keystroke features, and created classifiers for 15 emotional states that show promise for anger and excitement.
Journal ArticleDOI
Towards an automatic early stress recognition system for office environments based on multimodal measurements
TL;DR: This work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behaviouralmodalities, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.
Journal ArticleDOI
Emotion Recognition from Physiological Signal Analysis: A Review
TL;DR: In this article, an overview of methods to recognize emotions and to compare their applicability based on existing studies is given, which should enable practitioners, researchers and engineers to find a system most suitable for certain applications.
Proceedings ArticleDOI
Under pressure: sensing stress of computer users
TL;DR: This work explores the possibility of using a pressure-sensitive keyboard and a capacitive mouse to discriminate between stressful and relaxed conditions in a laboratory study and discusses the potential implications and recommendations for future work.
References
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Bradley Efron,Robert Tibshirani +1 more
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C4.5: Programs for Machine Learning
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