Automatic prediction of frustration

By Ashish Kapoor, Winslow Burleson, Rosalind W. Picard. Int. J. Hum. Comput. Stud., 2007

648 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/cc2b95298372c110b3eb9b8001e5aebe35d7dde2

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Citations in the corpus

Fundamentals of physiological computing (2009, background): “…may be grouped into three broad categories (Gilleade et al., 2005): (a) By offering assistance if the user is frustrated (Gilleade and Dix, 2004; Kapoor et al., 2007; Partala and Surrakka, 2004; Scheirer et al., 2002) or in a state of ''stuck " (Burleson and Picard, 2004) or unable to perform…”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Unfortunately, the Kapoor et al. (2007) study did not report single-channel classification accuracy; hence, it is difficult to assess the specific benefits of considering multiple channels.”
Robust Lightweight Facial Expression Recognition Network with Label Distribution Training (2021, background): “In the ﬁeld of education, detecting students’ frustration can help improve e-learning experience (Kapoor, Burleson, and Picard 2007).”
The Hourglass of Emotions (2011, background): “Few tentative efforts to detect non-basic affective states, such as fatigue, anxiety, satisfaction, confusion, or frustration, have been also made [17–22] (Table.”
Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments (2010, background): “One possibility is automatic detection of cognitive–affective states by computers (D’Mello et al., 2007; Kapoor et al., 2007; Prendinger and Ishizuka, 2005).”
Social Robots for Long-Term Interaction: A Survey (2013, background): “As studied by Kanda et al. [37], the incremental implementation of novel behaviours (e.g., self-disclosure) did play an important role for maintaining users engaged with Robovie.”
Affective Body Expression Perception and Recognition: A Survey (2013, background): “Students lose motivation when high levels of affective states such as frustration, anxiety, fear of failure, etc., are experienced [21], [22].”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “Unfortunately, the Kapoor et al. (2007) study did not report single channel classification accuracy, hence, it is difficult to assess the specific benefits of considering multiple channels.”
Affect-aware tutors: recognising and responding to student affect (2009, methodology): “Classifier algorithms predicted student frustration with 79% accuracy (Kapoor et al., 2007).”
Short-term emotion assessment in a recall paradigm (2009, background): “Fusion of these different modalities to improve the recognition accuracy has also been studied (Kapoor et al., 2007; Kim et al., 2005; Pantic and Rothkrantz, 2003; Zeng et al., 2008).”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “, [Kapoor et al. 2007]), but this was unavoidable due to the nature of the analytic strategy.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “Detecting students’ frustration can help improve e-learning experiences [44].”
Human-Computer Interaction: Overview on State of the Art (2008, neutral): “One of the few works that has attempted to integrate more than one modality for affect recognition is [67] in which facial features and body posture features are combined to produce an indicator of one’s frustration.”