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A Predictive Linear Regression Model for Affective State Detection of Mobile Touch Screen Users

A Predictive Linear Regression Model for Affective State Detection of Mobile Touch Screen Users

Samit Bhattacharya
Copyright: © 2017 |Volume: 9 |Issue: 1 |Pages: 15
ISSN: 1942-390X|EISSN: 1942-3918|EISBN13: 9781522512790|DOI: 10.4018/IJMHCI.2017010103
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MLA

Bhattacharya, Samit. "A Predictive Linear Regression Model for Affective State Detection of Mobile Touch Screen Users." IJMHCI vol.9, no.1 2017: pp.30-44. https://doi.org/10.4018/IJMHCI.2017010103

APA

Bhattacharya, S. (2017). A Predictive Linear Regression Model for Affective State Detection of Mobile Touch Screen Users. International Journal of Mobile Human Computer Interaction (IJMHCI), 9(1), 30-44. https://doi.org/10.4018/IJMHCI.2017010103

Chicago

Bhattacharya, Samit. "A Predictive Linear Regression Model for Affective State Detection of Mobile Touch Screen Users," International Journal of Mobile Human Computer Interaction (IJMHCI) 9, no.1: 30-44. https://doi.org/10.4018/IJMHCI.2017010103

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Abstract

Emotion, being important human factor, should be considered to improve user experience of interactive systems. For that, we first need to recognize user's emotional state. In this work, the author proposes a model to predict the affective state of a touch screen user. The prediction is done based on the user's finger strokes. The author defined seven features on the basis of the strokes. The proposed predictor is a linear combination of these features, which the author obtained using a linear regression approach. The predictor assumes three affective states in which a user can be: positive, negative and neutral. The existing works on affective touch interaction are few and rely on many features. Some of the feature values require special sensors, which may not be present in many devices. The seven features we propose do not require any special sensor for computation. Hence, the predictor can be implemented on any device. The model is developed and validated with empirical data involving 57 participants performing 7 touch input tasks. The validation study demonstrates a high prediction accuracy of 90.47%.

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