Anxiety-based affective communication for implicit human–machine interaction

Rani, P. Arockia Jansi; Sarkar, Nilanjan; Adams, Julie A.

doi:10.1016/j.aei.2006.11.009

Cited by 67 publications

(38 citation statements)

References 30 publications

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“…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%

“…Almost all studies reviewed in this paper did not apply any form of filtering on autonomic nervous system signals 2 . However, to eliminate noise in EEG signals, two key techniques were found to have been employed by those studies reviewed: [32], [33], [34], [35] Sound [29], [30], [31] Real Life Scenarios [36], [37], [38], [39], [40] …”

Section: Pre-processing (Artefact Removal and Filtering)mentioning

confidence: 99%

“…The studies, which implemented classification algorithms for emotion recognition processes (Table 4), reached an average of 77.91% classification accuracy (±12.76%, with minimum of 50% (random classification) and maximum of 96.5%), calculated through crossvalidation. [25], [30], [34], [35], [36], [37], [38], [39] Heart Rate Spectral Power Ratio 11 [10], [23], [25], [31] 3 Subtraction of the two channels' raw signals 4 GSR low frequency is 0Hz to 2.4Hz 5 Asymmetric Spectral Power Density = ! !…”

Section: Affective Analysis Classification and Validationmentioning

confidence: 99%

“…racing car driving, emotional situation recalling, etc.) to elicit and measure various emotional experiences [36], [37], [38], [39], [40]. The average stimulation duration was reported as 170 seconds (±75.5, minimum 90 and maximum 240 seconds).…”

mentioning

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%

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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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