Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy

Abstract

Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical and psychological illnesses. The advancement of sensing technologies, such as Internet of Things, has contributed to the understanding and assisting events related to anxiety and stress. However, the main challenge is knowing which approaches can be used to better monitor these emotions and assist people. Based on a systematic literature review, this work analyzed studies both to determine how data is collected, and to monitor anxiety and stress levels. Two taxonomies synthesize the techniques mapped. The results indicated more emphasis on studying stress than anxiety and more focus on detecting anxiety and stress levels than on assisting the user. Among the main techniques to collect data, 62.5% of the studies used physiological data like heart data, and for data analysis techniques, 48% of the studies used Decision Trees.

Acknowledgement

The authors would like to thank FAPERGS (Foundation for the Supporting of Research in the State of Rio Grande do Sul), CNPq (National Council for Scientific and Technological Development) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. We would also like to thank the University of Vale do Rio dos Sinos (Unisinos) for supporting the development of the present study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Mendeley is accessible at the address https://www.mendeley.com

Additional information

Notes on contributors

Leonardo dos Santos Paula

Leonardo dos Santos Paula received a B.Sc. degree in computer science from the University of Vale do Rio dos Sinos (Unisinos), Rio Grande do Sul, Brazil. He started the M.Sc. degree in applied computing at Unisinos in 2021. Also, he works as a web developer at CWI Software.

Lucas Pfeiffer Salomão Dias

Lucas Pfeiffer Salomão Dias received the M.Sc. degree in applied computing from the University of Vale do Rio dos Sinos (UNISINOS), in 2018 and starts the Ph.D. degree at UNISINOS in 2020. He integrates a research team at Mobile Computing Laboratory (Unisinos) focused on Ubiquitous Computing and Health.

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 researcher at Unisinos University and has worked for over twenty years in software engineering subjects.

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 and University of California Irvine. Jorge is a full professor at University of Vale do Rio dos Sinos (UNISINOS).