Fighting game skill evaluation method using surface EMG signal

Taneichi, Takashi; Toda, Masashi

doi:10.1109/gcce.2012.6379550

Cited by 6 publications

(3 citation statements)

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“…Whether in consumer electronics or prosthetics, effectively switching between command sets for various application scenarios is crucial ( Zhang et al, 2009 ; Kim et al, 2016 ). For instance, in rehabilitation training based on game interaction, referring to diagnostic results to establish a rehabilitation prescription (command set) is often necessary ( Taneichi and Toda, 2012 ). However, the calibration process after switching sets can be excessively time-consuming and labor-intensive, particularly when recording data for new actions, which also requires a considerable amount of time ( Wang et al, 2023a ).…”

Section: Calibration Burden Scenariosmentioning

confidence: 99%

“…In recent years, MPR technology has found wider application in human-computer interaction and has even demonstrated potential in consumer electronic devices. For example, it is utilized in MI devices for virtual reality, gaming entertainment, and industrial control ( Zhang et al, 2009 ; Taneichi and Toda, 2012 ; Khalaf et al, 2020 ). Although the performance of myoelectric interfaces (MIs) is satisfactory under laboratory conditions, there are usually a variety of practical challenges in real-lift applications ( Parajuli et al, 2019 ; Stephanidis et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Robust myoelectric pattern recognition methods for reducing users’ calibration burden: challenges and future

Wang,

Ao,

2024

Front. Bioeng. Biotechnol.

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Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots. Current MIs not only enable multi-degree-of-freedom control of prosthetic limbs but also demonstrate substantial potential in consumer electronics. However, the non-stationary random characteristics of myoelectric signals poses challenges, leading to performance degradation in practical scenarios such as electrode shifting and switching new users. Conventional MIs often necessitate meticulous calibration, imposing a significant burden on users. To address user frustration during the calibration process, researchers have focused on identifying MPR methods that alleviate this burden. This article categorizes common scenarios that incur calibration burdens as based on data distribution shift and based on dynamic data categories. Then further investigated and summarized the popular robust MPR algorithms used to reduce the user’s calibration burden. We categorize these algorithms as based on data manipulate, feature manipulation and, model structure. And describes the scenarios to which each method is applicable and the conditions required for calibration. Finally, this review is concluded with the advantages of robust MPR and the remaining challenges and future opportunities.

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Section: Calibration Burden Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust myoelectric pattern recognition methods for reducing users’ calibration burden: challenges and future

Wang,

Ao,

2024

Front. Bioeng. Biotechnol.

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“…Within the domain of video games, there have been a few attempts at skill prediction, using a variety of techniques, including physiological monitoring, recording game events, and logging player input from the hardware. The first of these, monitoring physiological responses, explored skill in a fighting game [18]. The researchers distinguished between players of different skill using the performance metric 'success rate' when inputting commands.…”

Section: B Skill Predictionmentioning

confidence: 99%

Rapid Skill Capture in a First-Person Shooter

Buckley¹,

Chen²,

Knowles³

2014

Preprint

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Various aspects of computer game design, including adaptive elements of game levels, characteristics of 'bot' behavior, and player matching in multiplayer games, would ideally be sensitive to a player's skill level. Yet, while difficulty and player learning have been explored in the context of games, there has been little work analyzing skill per se, and how it pertains to a player's input. To this end, we present a data set of 476 game logs from over 40 players of a first-person shooter game (Red Eclipse) as a basis of a case study. We then analyze different metrics of skill and show that some of these can be predicted using only a few seconds of keyboard and mouse input. We argue that the techniques used here are useful for adapting games to match players' skill levels rapidly, perhaps more rapidly than solutions based on performance averaging such as TrueSkill.

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