Biofeedback Sensors in Electronic Games: A Practical Evaluation

Soares, Rodrigo Teixeira; Sarmanho, Elton; Miura, Marco Akira; Barros, Tiago Carneiro Gorgulho Mendes; Jacobi, Ricardo Pezzuol; Castanho, Carla Denise

doi:10.1109/sbgames.2017.00015

Cited by 6 publications

(8 citation statements)

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“…where P f is measured in a unit of ms 2 . The time complexity of DFT requires O(N 2 ), and a fast Fourier transform (FFT) is generally preferred for implementing the frequency domain calculation to achieve a running time of O(Nlog N).…”

Section: Ppg-based Hrv Measurementmentioning

confidence: 99%

“…However, questionnaire-based feedback is easily affected by individual differences and may not fit real-time applications. In order to achieve an objective and real-time measurement of user experience, biofeedback sensors can be considered to gather physiological signals of the player during the game [ 2 , 3 ]. Typical biofeedback sensors include electrocardiography (ECG), electromyography (EMG), electrodermal activity (EDA), electrooculography (EOG), and electroencephalography (EEG).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, building a connection between subjective flow experience and objective physiological signals becomes an essential challenge in the video game industry. In [ 2 , 9 ], it is shown that ECG and facial EMG signals can correspond to intense emotions (e.g., excitement and thrill) in a game. Specifically, the heart rate (HR) derived from ECG signals correlates with intense emotions up to 56% [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…In [ 2 , 9 ], it is shown that ECG and facial EMG signals can correspond to intense emotions (e.g., excitement and thrill) in a game. Specifically, the heart rate (HR) derived from ECG signals correlates with intense emotions up to 56% [ 2 ]. Cowley et al indicate that for serious games, the inter-beat interval (IBI) can be associated with the studying performance [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…In collecting objective physiological data, the PPG-based smart band measures the heartbeat signals of the participant during the game, and the corresponding HRV parameters are calculated. Most previous studies use time domain parameters such as HR and IBI [ 2 , 9 , 10 , 23 ]. This study considers the frequency domain parameter, namely LF/HF ratio, as the flow state indicator.…”

Section: Introductionmentioning

confidence: 99%

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Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study

Shih

You

2023

Sensors

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Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human’s emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model’s mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.

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Section: Ppg-based Hrv Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%