Understanding affective interaction: Emotion, engagement, and internet videos

Bardzell, Shaowen; Bardzell, Jeffrey; Pace, Tyler

doi:10.1109/acii.2009.5349551

Cited by 25 publications

(14 citation statements)

References 26 publications

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“…In another study, conducted by Bardzell [15], physiological measures, such as heart rate and respiration per minute, were used to measure the emotions caused by the most popular videos. They also used strategies such as emotional tag (user indicates their emotional state by choosing emoticons), space for adding a text review and logging the user actions with respect to the video (pause, stop, browse the timeline of video (seek), etc.).…”

Section: Related Workmentioning

confidence: 99%

“…• Video Reproduction: In this area it is important to evaluate video stream reproduction, which can be influenced by different factors, as it may have different users´ QoE (Quality of Experience) [16], and consequently the levels of engagement with the content ("viewer engagement") [15] [12] may vary. To estimate such subjective parameters, we can evaluate for example, the quality of the playback video (using the comparison of signal to noise ratio, NSR, the original video with the video received), the degree of user satisfaction, or user behavior during reproduction.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore the results of such evaluation will allow educational institutions to plan the financial and technological infrastructure necessary for the delivery of educational materials. Therefore, in order to obtain these parameters it is necessary to define appropriate metrics with which to measure them [14] [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the effectiveness of the use of teaching materials in video format in distance learning environments

Stiubiener

Silveira

Matushima

et al. 2012

2012 Frontiers in Education Conference Proceedings

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Projections indicate that video will be responsible for 90% of network traffic until the end of the decade. Considering the high cost of media production and, mainly, the high consumption of network resources for applications that explore it, it will be extremely important to monitor such use, preventing against wrong or distorted views of this consumption. Within this scenario, it is necessary to develop mechanisms and tools that provide a clear vision of video effectiveness consumption, as well as the measuring of "viewer engagement". What tools can be developed to evaluate such consumption? What should be measured, i.e., which are the parameters and metrics necessary, and how can we obtain them, in order to measure the effectiveness of video applications use, for example in engineering courses and distance learning environments? These are some of the questions considered in this paper, especially evaluated within a scenario of video usage for teaching/learning purpose.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the effectiveness of the use of teaching materials in video format in distance learning environments

Stiubiener

Silveira

Matushima

et al. 2012

2012 Frontiers in Education Conference Proceedings

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“…The significance of emotional factors was studied elsewhere [2], [4], [62]. When human factors are combined with other factors, the model trained on human and emotion factors outperform the others, giving the best performance in predicting enjoyment of videos.…”

Section: Predictive Modelsmentioning

confidence: 99%

Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Scott

Guntuku

Wang

et al. 2016

IEEE Trans. Multimedia

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The interplay between system, context and human factors is important in perception of multimedia quality. However, studies on human factors are very limited in comparison to those for system and context factors. This article presents an attempt to explore the influence of personality and cultural traits on perception of multimedia quality. As a first step, a database consisting of 144 video sequences from 12 short movie excerpts has been assembled and rated by 114 participants from a cross-cultural population.Thereby providing a useful ground-truth for this (as well as future) study. As a second step, three statistical models are compared: (i) a baseline model to only consider system factors; (ii) an extended model to include personality and culture; and (iii) an optimistic model in which each participant is modeled.As a third step, predictive models based on content, affect, system, and human factors are trained to generalize the statistical findings. As shown by statistical analysis, personality and cultural traits represent 9.3% of the variance attributable to human factors and human factors overall predict an equal or higher proportion of variance compared to system factors. Moreover, the quality-enjoyment correlation varies across the excerpts. Predictive models trained by including human factors demonstrate about 3% and 9% improvement over models trained solely based on system factors for predicting perceived quality and enjoyment. As evidenced by this, human factors indeed are important in perceptual multimedia quality, but the results suggest further investigation of moderation effects and a broader range of human factors is necessary.

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“…This pulsimeter has been used in other studies before [38]. The data needed by the application are within the following three frames:

Breathing Waveform Packet: shows the variation of the user's respiration.

…”

Section: Methodsmentioning

confidence: 99%

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

Villarejo

Zapirain

Zorrilla

2013

Sensors

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This paper presents the results of using a commercial pulsimeter as an electrocardiogram (ECG) for wireless detection of cardiac alterations and stress levels for home control. For these purposes, signal processing techniques (Continuous Wavelet Transform (CWT) and J48) have been used, respectively. The designed algorithm analyses the ECG signal and is able to detect the heart rate (99.42%), arrhythmia (93.48%) and extrasystoles (99.29%). The detection of stress level is complemented with Skin Conductance Response (SCR), whose success is 94.02%. The heart rate variability does not show added value to the stress detection in this case. With this pulsimeter, it is possible to prevent and detect anomalies for a non-intrusive way associated to a telemedicine system. It is also possible to use it during physical activity due to the fact the CWT minimizes the motion artifacts.

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Understanding affective interaction: Emotion, engagement, and internet videos

Cited by 25 publications

References 26 publications

Evaluating the effectiveness of the use of teaching materials in video format in distance learning environments

Evaluating the effectiveness of the use of teaching materials in video format in distance learning environments

Do Personality and Culture Influence Perceived Video Quality and Enjoyment?

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

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