An empirical study of machine learning techniques for affect recognition in human–robot interaction

By Changchun Liu, Pramila Rani, Nilanjan Sarkar. 2005

361 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

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In Experimental and Cognitive Psychology

In Social Psychology

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Several authors argue that explain-able models might ease the task of ﬁnding relationships that, should they occur, could be tested further for a stronger causal link between the involved variables [159,160] .”
The mythos of model interpretability (2016, neutral): “But ethics and legality don’t admit direct optimization.”
Fundamentals of physiological computing (2009, methodology): “This assessment may be achieved via the development of discriminant algorithms (Liu et al., 2005) or neural networks (Gevins et al.”
Short-term emotion assessment in a recall paradigm (2009, background): “In many results it can also be seen that EMG signals are usable for emotion assessment (Rainville et al., 2006; Rani et al., 2006; Sinha and Parsons, 1996; van den Broek et al., 2006; Wagner et al., 2005).”
Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty (2011, background): “[7] proposed to classify three levels of intensity for different emotions.”
Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings (2019, neutral): “SVM has been successfully used in emotion classification [44], [45].”
Experience-Driven Procedural Content Generation (2011, neutral): “Up until recently, PCG was almost only used in narrowly specialized roles and almost always during the development of the game; in the last few years, more and more games feature runtime PCG of some sort.”
Emotion in Human-Computer Interaction (2012, background): “More accurate results can usually be obtained with support vector machines (SVM) (Barreto et al. 2007; Kim et al. 2004; Rani et al. 2006) and Bayesian networks (Rani et al.”
Robotics and Autonomous Systems (2014, neutral): “The interpretation of emotions and other social messages has been confined to facial expression [218,219], acoustic information [220], audiovisual data [221], physiological signals [222,223] and touch through ordinary haptic devices, such as a computer mouse [224].”
EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, methodology): “As can be seen here, SVM and KNN were among the more popular methods for emotion classiﬁcation and the highest achieved performance was 97.33% (SVM) and 98.37% (KNN).”