Publication Type

Conference Proceeding Article

Publication Date

1-2016

Abstract

Understanding emotions of gamers can benefit game designers in various ways. How gamers feel while they playing a game can be treated as valuable user feedback to improve the development process of that game. Sensing player emotions also enables game designers to create adaptive game that can adjust itself to provide best gaming experience based on player emotions. However, how to effectively evaluate emotions of gamers is still an open research challenge. Two common techniques to evaluate emotional state are using self-assessments such as questionnaires or interviews, and to recognize expressed emotions by analyzing videos or images of facial expression, body languages, and gesture. Although the self-report approach is convenient and unobtrusive, it requires users' cognitive attention. The vision-based approach also has some drawbacks that users' expressions do not necessarily reflect what they actually feel and there is also concern about privacy as images of users are captured. In this work, we implement Jasper, a system using on wearable physiological sensors to recognize emotions of gamers. Compared to the mentioned approaches, physiological sensors system can recognize emotions continuously without interrupting user experience. Moreover, the physiological signals are involuntary and mostly activated by the Autonomic Nervous System, which are useful to detect actual emotions and robust to expression manipulation (e.g. social making). The contribution of this work is to demonstrate the feasibility of using commodity and unobtrusive wearable biosensors to recognize mobile gamer emotions.

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

MobiSys '16 Companion: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion, Singapore, June 26-30, 2016

First Page

104

Last Page

104

ISBN

9781450344166

Identifier

10.1145/2938559.2938576

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/2938559.2938576

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