Affective computing in the modern workplace
Affective computing refers to a class of computer-based solutions that analyse data about human behaviour, such as facial expressions, gestures, and language, for its emotional information. The term was first coined 25 years ago when the ability for computers to perform basic sensorimotor tasks such...
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Published in | Business information review Vol. 37; no. 2; pp. 78 - 85 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
London, England
SAGE Publications
01.06.2020
Bowker-Saur Ltd |
Subjects | |
Online Access | Get full text |
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Abstract | Affective computing refers to a class of computer-based solutions that analyse data about human behaviour, such as facial expressions, gestures, and language, for its emotional information. The term was first coined 25 years ago when the ability for computers to perform basic sensorimotor tasks such as object detection in images was in its infancy. This article revisits the subject and considers how it is being applied in real-world applications today. We look at research from cognitive science informing our understanding of emotions and how computing capabilities have advanced in recent years to produce cognitive algorithms capable of detecting human attention, emotion and health. Affective computing offers the potential to revolutionise how we incorporate emotion as information in communications and decision systems. However, much of the underlying research that forms the foundations for emotion detection is being challenged, raising concerns about the ethics, trustworthiness and viability of such platforms. This article presents a critique of the technologies and appraises their suitability for real-world applications as part of a modern workplace. |
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AbstractList | Affective computing refers to a class of computer-based solutions that analyse data about human behaviour, such as facial expressions, gestures, and language, for its emotional information. The term was first coined 25 years ago when the ability for computers to perform basic sensorimotor tasks such as object detection in images was in its infancy. This article revisits the subject and considers how it is being applied in real-world applications today. We look at research from cognitive science informing our understanding of emotions and how computing capabilities have advanced in recent years to produce cognitive algorithms capable of detecting human attention, emotion and health. Affective computing offers the potential to revolutionise how we incorporate emotion as information in communications and decision systems. However, much of the underlying research that forms the foundations for emotion detection is being challenged, raising concerns about the ethics, trustworthiness and viability of such platforms. This article presents a critique of the technologies and appraises their suitability for real-world applications as part of a modern workplace. |
Author | Richardson, Sharon |
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Cites_doi | 10.1002/1520-6696(198004)16:2<107::AID-JHBS2300160202>3.0.CO;2-Y 10.1016/j.autcon.2017.02.001 10.1093/mind/LIX.236.433 10.1177/1754073910374661 10.18653/v1/P19-1163 |
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References_xml | – volume: 16 start-page: 107 year: 1980 end-page: 117 article-title: The trilogy of mind: cognition, affection, and conation publication-title: Journal of the History of the Behavioral Sciences – volume: 321 year: 1995 article-title: Affective computing publication-title: MIT Media Laboratory Perceptual Computing Section Technical Report No – volume: 2 start-page: 363 issue: 4 year: 2010 end-page: 370 article-title: The many meanings/aspects of emotion: definitions, functions, activation and regulation publication-title: Emotion Review – year: 2019 article-title: ‘Emotion AI’ is a $20 billion business. New research says it can’t do what it claims publication-title: The Washington Post – volume: 82 start-page: 122 year: 2017 end-page: 137 article-title: Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system publication-title: Automation in Construction – volume: 49 start-page: 433 year: 1950 end-page: 460 article-title: Computing machinery and intelligence publication-title: Mind – ident: bibr4-0266382120930866 – year: 2019 ident: bibr21-0266382120930866 publication-title: The Washington Post – ident: bibr12-0266382120930866 – ident: bibr17-0266382120930866 – ident: bibr8-0266382120930866 doi: 10.1002/1520-6696(198004)16:2<107::AID-JHBS2300160202>3.0.CO;2-Y – ident: bibr23-0266382120930866 – ident: bibr9-0266382120930866 – ident: bibr26-0266382120930866 doi: 10.1016/j.autcon.2017.02.001 – ident: bibr14-0266382120930866 – ident: bibr6-0266382120930866 – volume-title: How Emotions Are Made: The Secret Life of the Brain year: 2017 ident: bibr2-0266382120930866 – ident: bibr18-0266382120930866 – ident: bibr25-0266382120930866 doi: 10.1093/mind/LIX.236.433 – ident: bibr1-0266382120930866 – ident: bibr11-0266382120930866 – volume-title: Descartes’ Error: Emotion, Reason, and the Human Brain year: 1994 ident: bibr3-0266382120930866 – ident: bibr22-0266382120930866 – ident: bibr10-0266382120930866 doi: 10.1177/1754073910374661 – volume-title: Concise Oxford English Dictionary, Eleventh Edition year: 2004 ident: bibr15-0266382120930866 – volume-title: Life 3.0: Being Human in the Age of Artificial Intelligence year: 2017 ident: bibr20-0266382120930866 – ident: bibr27-0266382120930866 – ident: bibr19-0266382120930866 doi: 10.18653/v1/P19-1163 – ident: bibr5-0266382120930866 – ident: bibr13-0266382120930866 – ident: bibr24-0266382120930866 – volume: 321 year: 1995 ident: bibr16-0266382120930866 publication-title: MIT Media Laboratory Perceptual Computing Section Technical Report No – ident: bibr7-0266382120930866 |
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Title | Affective computing in the modern workplace |
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