Analyzing repetitive action in game based on sequence pattern matching

Kang, Seong-Hoon; Kim, Young Bin; Kim, Soo Kyun

doi:10.1007/s11554-013-0347-0

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…To identify the facial expressions in players, this study detected facial feature points and established methods for tracking them. Research is available on the detection of such facial feature points ( Wang et al, 2014 ) and it has been used in various fields ( Gu & Ji, 2004 ; Kang, Kim & Kim, 2014 ; Kaulard et al, 2012 ; Li et al, 2013 ; Pejsa & Pandzic, 2009 ). There are a number of commercial solutions related to these research results ( Pantic & Bartlett, 2007 ).…”

Section: Methodsmentioning

confidence: 99%

“…Using this data, keyboard and mouse patterns and usage can be recognized, and the proficiency of the user can be inferred to judge the level of difficulty. Based on the proficiency and the level of difficulty expressed by the user, the level of immersion in a particular game could be measured indirectly ( Ermi & Mäyrä, 2005 ; Kang, Kim & Kim, 2014 ; Kim, Kang & Kim, 2013 ). This can be used to better understand how the UI is used.…”

Section: Methodsmentioning

confidence: 99%

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Efficiently detecting outlying behavior in video-game players

Kim

Kang

Lee

et al. 2015

PeerJ

Self Cite

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In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Efficiently detecting outlying behavior in video-game players

Kim

Kang

Lee

et al. 2015

PeerJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…Others, in turn, used more large‐scale datasets and relied on sequence mining techniques to analyze gameplay. For instance, Kang et al [KKK14] analyzed how abilities get concatenated by players in League of Legends [Rio09]. Leece and Jhala [LJ14] employed sequential pattern mining to derive common action patterns and build orders from StarCraft: Brood War [Bli98].…”

Section: Related Workmentioning

confidence: 99%

“…[CMTD18, MG11, Wal15]). Existing work has employed various sequence mining [KKK14, LJ14] and statistical techniques [Wal15, Hou12] to obtain insights into behavioral sequences. Visualization solutions to help analyze and explore sequences in games are, however, much more scarce (e.g., [OSM18, LFLB19]).…”

Section: Introductionmentioning

confidence: 99%

Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game

Agarwal

Wallner

Beck

2020

Computer Graphics Forum

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Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline‐based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration.

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“…The methods of detection of such facial feature points 19 and the applications in various fields have been reported previously. [20][21][22] There are a number of commercial solutions related to these research results. 23 Facial feature points are modeled using an AAM, which is one of the best-known facial feature extraction techniques.…”

Section: Facial Expression Classificationmentioning

confidence: 99%

A visual-physiology multimodal system for detecting outlier behavior of participants in a reality TV show

Kang

Kim

2019

International Journal of Distributed Sensor Networks

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This study proposes an outlier detection system based on the visual-physiology multimodal data system for a Korean reality TV show, “Perfect on Paper.” The goal of this system is to provide the program production team and the master of ceremonies with real-time facial expressions and bio-signal analysis results of program participants. Using this information, the program production team and master of ceremonies can easily identify the participants with unusual behavior. We propose an insole-type hardware that measures the wearer’s skin conductivity, temperature, and motion. We also suggest a dynamic time warp-based clustering algorithm for the outlier and micro-expression detection technique. The system was developed starting from the program planning phase through a 6-month period in collaboration with the production team. The developed system analyzed the biometric information and facial expressions of 88 participants in 11 episodes and provided rapid feedback to the production team at the shooting spot in real time. Finally, we present a case where a visual-physiology multimodal system can be useful for a real TV broadcasting production.

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Analyzing repetitive action in game based on sequence pattern matching

Cited by 8 publications

References 8 publications

Efficiently detecting outlying behavior in video-game players

Efficiently detecting outlying behavior in video-game players

Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game

A visual-physiology multimodal system for detecting outlier behavior of participants in a reality TV show

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