Context Awareness in Recognition of Affective States: A Systematic Mapping of the Literature

Abstract

Studies indicate the growth and relevance of systems that can recognize affective states in the most different areas. A tendency to more natural environments could be noticed that considering physical or physiological signs and the individual’s relationship with the environment, other people, and daily activities. Therefore, what is the importance of combining ubiquitous computing, affective computing, and the contributions of context-aware information to provide more accurate and intelligent affective systems? This study presents a systematic literature mapping about the use of contextual information to identify affective states, which covered articles published between 2010 and October 2021, resulting in 1.638 studies. After applying filters, which we explain further in this article, we selected 49 works to answer a set of research questions. The results indicate that physiological data was the main parameter for recognizing affective signs (62.3%, 33/53), followed by visual data (32.1%, 17/53). The links between context and affective signs presented a more significant occurrence in the combination of contexts related to activities and physiological data (34%, 18/53).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this article.

Acknowledgments

The authors would like to thank the Federal Institute of Education, Science, and Technology of Rio Grande do Sul (IFRS), the National Development Council Scientific and Technological (CNPq), the University of Vale do Rio dos Sinos (Unisinos), the FEEVALE University and, especially acknowledge the support of the Applied Computing Graduate Program (PPGCA) of Unisinos.

Notes

1 https://www.zotero.org/support/duplicate_detection.

2 https://github.com/sandrodorneles/planilhas/blob/main/Fulldata.xlsx.

Additional information

Notes on contributors

Sandro Oliveira Dorneles

Sandro Oliveira Dorneles received M.Sc. in Computing Applied from the University of Vale do Rio dos Sinos (UNISINOS), Brazil. Sandro is a full professor of the Federal Institute of Education, Science, and Technology of Rio Grande Sul (IFRS).

Rosemary Francisco

Rosemary Francisco is Ph.D. in Business Administration with a doctoral internship at the University of Helsinki, Finland. She is a professor and a researcher at Unisinos University and has been working for more than 20 years in software engineering subjects.

Débora Nice Ferrari Barbosa

Débora Nice Ferrari Barbosa received M.Sc. and Ph.D. in computer science from the Federal University of Rio Grande do Sul (UFRGS), Brazil. She conducted post-doctoral studies at the University of California Irvine (USA). Débora is a full professor and researcher of the Feevale University (FEEVALE).

Jorge Luis Victória Barbosa

Jorge Luis Victória Barbosa received M.Sc. and Ph.D. in computer science from the Federal University of Rio Grande do Sul, Brazil. He conducted post-doctoral studies at Sungkyunkwan University (South Korea) and University of California Irvine (USA). Jorge is a full professor at the University of Vale do Rio dos Sinos (UNISINOS).