Key result
Classification models using galvanic skin response and heart rate from low-cost wearable devices detected game users' flow experience with over 90% accuracy for two-way classification.
Why the study?
Studies assessing game user experience via psychophysiological responses remain scarce, limited in scope, and hindered by low detection accuracy and dependence on professional physiological signal apparatus.
Low-cost wearable devices measuring galvanic skin response and heart rate can effectively classify the flow experience of game users with high accuracy.
Relevant research has shown the potential to understand the game user experience (GUX) more accurately and reliably by measuring the user’s psychophysiological responses. However, the current studies are still very scarce and limited in scope and depth. Besides, the low-detection accuracy and the common use of the professional physiological signal apparatus make it difficult to be applied in practice. This article analyzes the GUX, particularly flow experience, based on users’ physiological responses, including the galvanic skin response (GSR) and heart rate (HR) signals, captured by low-cost wearable devices. Based on the collected data sets regarding two test games and the mixed data set, several classification models were constructed to detect the flow state automatically. Hereinto, two strategies were proposed and applied to improve classification performance. The results demonstrated that the flow experience of game users could be effectively classified from other experiences. The best accuracies of two-way classification and three-way classification under the support of the proposed strategies were over 90% and 80%, respectively. Specifically, the comparison test with the existing results showed that Strategy1 could significantly reduce the negative interference of individual differences in physiological signals and improve the classification accuracy. In addition, the results of the mixed data set identified the potential of a general classification model of flow experience.
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Ye et al. (2020) studied Game user experience (flow state). Physiological responses capture (GSR and HR) via wearable devices was evaluated on Flow state classification accuracy. Classification models using galvanic skin response and heart rate from low-cost wearable devices detected game users' flow experience with over 90% accuracy for two-way classification.
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