Using psychophysiological techniques to measure user experience with entertainment technologies

Mandryk, Regan L.; Inkpen, Kori; Calvert, Tom

doi:10.1080/01449290500331156

Cited by 427 publications

(269 citation statements)

References 24 publications

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“…In the affective computing field [12], various studies have been carried out to create systems that can recognize the affective states of their user by analyzing their vocal [1], facial [11] [17], and body expressions [4], and even their physiological changes [6]. Most of the work has been carried out on acted or stereotypical expressions.…”

Section: Introductionmentioning

confidence: 99%

Naturalistic Affective Expression Classification by a Multi-stage Approach Based on Hidden Markov Models

Meng

Bianchi-Berthouze

2011

Affective Computing and Intelligent Interaction

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Abstract. In naturalistic behaviour, the affective states of a person change at a rate much slower than the typical rate at which video or audio is recorded (e.g. 25fps for video). Hence, there is a high probability that consecutive recorded instants of expressions represent a same affective content. In this paper, a multi-stage automatic affective expression recognition system is proposed which uses Hidden Markov Models (HMMs) to take into account this temporal relationship and finalize the classification process. The hidden states of the HMMs are associated with the levels of affective dimensions to convert the classification problem into a best path finding problem in HMM. The system was tested on the audio data of the Audio/Visual Emotion Challenge (AVEC) datasets showing performance significantly above that of a one-stage classification system that does not take into account the temporal relationship, as well as above the baseline set provided by this Challenge. Due to the generality of the approach, this system could be applied to other types of affective modalities.

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Section: Introductionmentioning

confidence: 99%

Naturalistic Affective Expression Classification by a Multi-stage Approach Based on Hidden Markov Models

Meng

Bianchi-Berthouze

2011

Affective Computing and Intelligent Interaction

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“…Dimensions that are addressed include emotions [36,43], social factors [15], values and requirements [5,42,44], the perception of the quality of the interaction or representation [53,47], or service quality and content [16,31]. These user experience evaluations have been applying standard UX methods, like the AttrakDiff questionnaire [45].…”

Section: User Experience Evaluation Of Interactive Tvmentioning

confidence: 99%

“…Other measurements include the SUXES evaluation method, as used by Turunen et al [53] to evaluate UX with different modalities in a mobile phone controlled home entertainment system. Experiences in gaming and TV applications were evaluated using psycho-physiological measurements [36]. These measurements were also used to evaluate the users' experiences with multi-view 3D displays [47] or to classify emotional reactions to video content [43].…”

Section: User Experience Evaluation Of Interactive Tvmentioning

confidence: 99%

Evaluating User Experience for Interactive Television: Towards the Development of a Domain-Specific User Experience Questionnaire

Bernhaupt

Pirker²

2013

Human-Computer Interaction – INTERACT 2013

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Abstract. This paper presents a questionnaire-based approach to evaluate the user experience (UX) while interacting with interactive Television (iTV) systems. Current contributions in the field of UX propose generic methods applicable to various application domains, whereas our contribution is dedicated to the specific domain of interactive TV systems. Based on a classification of UX dimensions from a literature review, the first version of the questionnaire is focusing on the dimension's aesthetics, emotion, stimulation and identification. A validation study with 106 participants was performed to assess the relations between the evaluated UX dimensions, as well as their fit to the underlying theoretical assumptions. Results showed that the UX dimensions aesthetics, emotion and stimulation are important for the domain of iTV, while identification was not confirmed. The study revealed significant correlations between the type of IPTV system used and the emotional and stimulation dimension. Additionally, a significant effect of the TV reception mode and the type of IPTV box owned on the emotion towards the system was observed. Beyond the contribution of the questionnaire that is directly applicable for any iTV system, the findings described in the paper demonstrate the need for user experience evaluation methods targeted at specific domains: the validation of the questionnaire shows that identification is not a central dimension of user experience when interacting with interactive TV.

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“…To obtain the values of signal prediction for our datapoints, we used the mean of the raw EMG signal as the base for signal analysis, because it is a measure that has consistently shown a reliable mapping with affective valence (e.g., [4,11). The standard method for generating valence predictions from EMG is to compare the EMG signal over the interval of interest against a baseline recorded during a resting period before the experiment.…”

Section: Creating Predictions From the Emg Signalmentioning

confidence: 99%

“…In contrast, most work on affect recognition has focused on detecting one specific emotion (e.g., [4][5][6]), lower-level affective measures of valence and arousal (e.g., [7,9]) or overall emotional predisposition over a complete interaction (e.g., [10,11]). One exception is the work by D'Mello et al, [12], which used dialogue features as predictors of student's boredom, confusion, flow and frustration during interaction with a dialogue-based tutoring system.…”

Section: Introductionmentioning

confidence: 99%

Modeling User Affect from Causes and Effects

Conati

Maclaren

2009

User Modeling, Adaptation, and Personalization

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Abstract. We present a model of user affect to recognize multiple user emotions during interaction with an educational computer game. Our model deals with the high level of uncertainty involved in recognizing a variety of user emotions by probabilistically combining information on both the causes and effects of emotional reactions. In previous work, we presented the performance and limitations of the model when using only causal information. In this paper, we discuss the addition of diagnostic information on user affective valence detected via an EMG sensor, and present an evaluation of the resulting model.

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Using psychophysiological techniques to measure user experience with entertainment technologies

Cited by 427 publications

References 24 publications

Naturalistic Affective Expression Classification by a Multi-stage Approach Based on Hidden Markov Models

Naturalistic Affective Expression Classification by a Multi-stage Approach Based on Hidden Markov Models

Evaluating User Experience for Interactive Television: Towards the Development of a Domain-Specific User Experience Questionnaire

Modeling User Affect from Causes and Effects

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