Automated Posture Analysis for Detecting Learner's Interest Level

By Selene Mota, Rosalind W. Picard. 2003 Conference on Computer Vision and Pattern Recognition Workshop, 2003

366 citations recorded by the source corpus.

Subfields: Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/fab11717be6416421c09e178f6c55e83dde93952

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Social Psychology

In Human-Computer Interaction

Citations in the corpus

Sentiment analysis in organizational work: Towards an ontology of people analytics (2018, background): “…2014); Posture identification (Quwaider & Biswas, 2008; Mattmann et al., 2008); Posture analysis to identify emotions (Rosário & Diógenes, 2014; Rosario & Diógenes, 2016) and measure learners' interest level (Mota & Picard, 2003); Analysis of sitting positions by sensors installed on chairs.”
Fundamentals of physiological computing (2009, background): “Similarly, observable behavioural expressions such as fidgeting, head shakes (Kapoor et al., 2007), changes in body posture (Mota and Picard, 2003) or pressure exerted to manipulate input control devices (Dennerlein et al., 2003) could be used as independent indicators of user state for the…”
Automatic prediction of frustration (2007, methodology): “2(b) shows the feature extraction strategy used for postures in Mota and Picard (2003).”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Instead of perseverating on such questions, Picard advocated a focus on the issues that are necessary for the development of affect-sensitive computer applications.”
Automatic detection of learner’s affect from conversational cues (2008, background): “2002; Matsubara and Nagamachi 1996) or ratings by independent judges (Liscombe et al. 2005; Litman and Forbes-Riley 2004; Mota and Picard 2003), but we propose the combination of several different measures of a learner’s affect.”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “Mota and Picard (2003) developed a model that could infer a student's interest from their posture.”
Multimodal affect recognition in learning environments (2005, methodology): “Exceptions include the work of Picard et al.”
Detection of Emotions during Learning with AutoTutor (2006, background): “Posture may be correlated with interest (Mota & Picard, 2003).”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, background): “…Koshizen t al., 2007) for curiosity detection, e.g., for topic switching in infotainment systems or in customer service systems; ultimodal (Stiefelhagen et al., 2002) and audivisual interest detection in meetings (Gatica-Perez et al., 2005); or children’s) tutoring systems (Mota and Picard, 2003).”
Affect-aware tutors: recognising and responding to student affect (2009, methodology): “The system achieved an accuracy of 76% on affect category recognition from chair pressure patterns, and 88% on nine ‘basic’ postures that were identified as making up the affective behaviours (Mota and Picard, 2003).”
Automatic analysis of multimodal group actions in meetings (2005, background): “Recent research in recognizing emotion from speech [33], [34], recognizing interest level from posture [35], recognizing hot-spots (regions of high involvement or emphasis) in meetings [36], [37], [38], and detecting agreement and disagreement in meetings [39], suggests that the automatic recognitio”
Gaze tutor: A gaze-reactive intelligent tutoring system (2012, background): “There might be aspects of engagement that might be manifested in physiology, facial expressions, and posture (D’Mello and Graesser, 2010b; Mota and Picard, 2003), and the present focus on gaze patterns risks overlooking these alternate manifestations of engagement.”