Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications

Kukolja, Davor; Popović, Siniša; Horvat, Marko; Kovač, Bernard; Osić, Krešimir

doi:10.1016/j.ijhcs.2014.05.006

Cited by 58 publications

(43 citation statements)

References 37 publications

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“…To prevent such incidents, the authors argue that the emotional stimuli must be subjectively evaluated and categorised prior to the undertaking of physiological measurements, in order to validate their effectiveness in evoking the required emotional experiences on the part of all users. So far various affective stimuli types have been employed in order to perform human emotional experience assessment and classification (summarised in [25], [26], [27]; whilst a smaller number were found to employ other image datasets, such as affective face gestures [28]. The average image presentation duration was reported as 14.5 seconds (±22.88, minimum 1 and maximum 60 seconds).…”

Section: Stimuli and Physiological Measurementsmentioning

confidence: 99%

“…Electroencephalography ( [25], [29], [30], [31], [35], [36], [37], [38], [39] Galvanic Skin Response (GSR) [25], [30], [32], [34], [35], [36], [37], [38], [39] Respiratory ( [33], [40]. This was due to the fact that eye-blinking artefacts are mainly observed in frequencies lower that 4Hz, as people rarely blink more than 4 times a second.…”

Section: Physiological Measurement Used In Studiesmentioning

confidence: 99%

“…The average stimulation duration was reported as 170 seconds (±75.5, minimum 90 and maximum 240 seconds). [26], [28], [29], [33], [34], [40] [25], [29], [30], [31], [35], [36], [37], [38], [39] [25], [30], [32], [34], [35], [36], [37], [38], [39] [25], [30], [34], [37], [38] [29], [30], [35], [37], [38], [39] …”

mentioning

confidence: 99%

“…As an illustration, if participants were exposed to affective images for 5 seconds, the affective features were extracted from the 5-second affective period. Similarly, if participants were exposed to affective music videos for 60 seconds, the affective features were extracted from the entire 60-second affective period [25], [28], [29], [33], [35], [39], [40].…”

mentioning

confidence: 99%

“…[2], [3] employed this technique to normalise the extracted features across all participants' observations, while [20], [25], [35] used this algorithm to normalise the raw recorded signals within each participant's observation (either within each window or across the entire stimuli duration).…”

mentioning

confidence: 99%

See 4 more Smart Citations

Affective Recognition for Multimedia Environments A Review

Moghimi¹,

Stone²,

Rotshtein³

2017

Preprint

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-Detecting emotional responses in multimedia environments is an academically and technologically challenging research issue. In the domain of Affective Computing, from non-interactive and static stimuli (e.g. affective image) to highly interactive and dynamic environments (affective virtual realities), researchers have employed a wide range of affective stimuli to measure and interpret human psychological and physiological emotional behaviours. Various psychophysiological parameters (e.g. Electroencephalography, Galvanic Skin Response, Heart Rate, etc.) have been employed and investigated, in order to detect and quantify human affective states. In this paper, we present a detailed literature review of over 33 affective computing studies, undertaken since 1993. All aspects of these studies (stimuli type, pre-processing, windowing, features, classification technique, etc.) have been reported in detail. We believe that this paper not only summarises the breadth of research over the past 20 years, but also serves to clarify various significant aspects and details of this increasingly valuable and relevant research area.

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Section: Stimuli and Physiological Measurementsmentioning

confidence: 99%

Section: Physiological Measurement Used In Studiesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Affective Recognition for Multimedia Environments A Review

Moghimi¹,

Stone²,

Rotshtein³

2017

Preprint

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Assessment of Mental Stress Through the Analysis of Physiological Signals Acquired From Wearable Devices

Zanetti

Faes

Cecco

et al. 2019

Lecture Notes in Electrical Engineering

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Survey on AI-Based Multimodal Methods for Emotion Detection

Maréchal

Mikołajewski

Tyburek

et al. 2019

Lecture Notes in Computer Science

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Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.

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Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications

Cited by 58 publications

References 37 publications

Affective Recognition for Multimedia Environments A Review

Affective Recognition for Multimedia Environments A Review

Assessment of Mental Stress Through the Analysis of Physiological Signals Acquired From Wearable Devices

Survey on AI-Based Multimodal Methods for Emotion Detection

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