Table 2.

Examples of technologies involved in automated emotion recognition

Technology	Description
Body language and gesture recognition	Recognition of meaningful body movements involving the fingers, hands, face, head or body (Mitra and Acharya 2007; Kleinsmith and Bianchi-Berthouze 2013)
Facial expression analysis	Measurement and interpretation of facial expressions (Zeng et al. 2009; Sariyanidi et al. 2015)
Facial recognition	Recognition of human faces, including if background clutter and variable image quality (Zhao et al. 2003; McPherson et al. 2016)
Natural language processing	Automatic extraction of meaning from human languages, both text and speech, requiring ambiguity resolution (Nadkarni et al. 2011)
Pattern recognition	Automated recognition, description, and classification of patterns, often involving statistical classification and neural networks (Jain et al. 2000)
Sensors	Identification of emotion using physiological signals such as heart rate, breathing, skin conduction, physical activity (Calvo and D’Mello 2010; Jerritta et al. 2011; Sun et al. 2010)
Sentiment analysis	Binary classification of subjective opinions in text such as positive versus negative, like versus dislike (Liu 2010)
Smartphone usage patterns	Identification of mood based on measures such as number and duration of incoming/outgoing calls; outgoing text messages, app usage (LiKamWa et al. 2013; Faurholt-Jepsen et al. 2016)
Speech emotion recognition	Recognition of the emotional content of human speech (El Ayadi et al. 2011; Zeng et al. 2009)
Speech recognition	Identification and understanding of human speech, converting into text or commands (Meng et al. 2012; Xiong et al. 2016)