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Flow Experience Det...
Flow Experience Detection and Analysis for Game Users by Wearable-Devices-Based Physiological Responses Capture
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- Ye, Xiaozhen (author)
- School of Computer and Communication Engineering, University of Science and Technology Beijing, China / Shunde Graduate School, University of Science and Technology Beijing, China
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- Ning, Huansheng (author)
- School of Computer and Communication Engineering, University of Science and Technology Beijing, China / Shunde Graduate School, University of Science and Technology Beijing, China
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- Backlund, Per, 1964- (author)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningsmiljön Informationsteknologi,Interaction Lab (ILAB),Högskolan i Skövde, Institutionen för informationsteknologi
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- Ding, Jianguo (author)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningsmiljön Informationsteknologi,Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems,Högskolan i Skövde, Institutionen för informationsteknologi
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(creator_code:org_t)
- IEEE, 2021
- 2021
- English.
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In: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 8:3, s. 1373-1387
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Games
- Physiology
- Electrocardiography; Heart rate
- Human computer interaction
- Task analysis
- Stress
- Flow
- game user experience (GUX)
- games
- physiological responses
- wearable devices
- Interaction Lab (ILAB)
- Interaction Lab (ILAB)
- Distribuerade realtidssystem (DRTS)
- Distributed Real-Time Systems
Publication and Content Type
- ref (subject category)
- art (subject category)
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