How to design adaptive systems to improve stress management using artificial intelligence

Authors

  • Arturo Morales CICESE UT3
  • Concepcion Valdez CICESE
  • Yanitza Stambor CICESE
  • Luis A. Castro Instituto Tecnológico de Sonora (ITSON)
  • Monica Tentori CICESE

DOI:

https://doi.org/10.47756/aihc.y9i1.153

Keywords:

Biofeedback, Sports, Design considerations, Heart-Rate Variability, Machine Learning, Physiological signals

Abstract

Biofeedback is a technique that relies on measuring bodily functions and providing feedback to the individual so that they can train and control those functions. Artificial intelligence has empowered these systems by making them context-aware, adapting models to users' physiological variations, and providing personalized feedback. However, incorporating AI techniques has opened up new challenges in designing, developing, and evaluating biofeedback systems. In this work, we conducted 25 semi-structured interviews with various specialists in medicine, psychology, human-computer interaction, and computer science to investigate what an AI-based system that considers differences in personal health data should look like. The results helped us answer ‘How can we design AI-assisted systems that take into account differences in personal physiological and AI knowledge between individuals to avoid misinterpretations?’ by defining six design considerations for biofeedback systems that use AI techniques trained with users' physiological signals. Finally, we discuss how these considerations could help researchers design systems for well-being.

Downloads

Download data is not yet available.

References

Barbara B Brown. 1977. Stress and the art of biofeedback. Harper & Row.

Yekta Said Can, Bert Arnrich, and Cem Ersoy. 2019. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of biomedical informatics 92 (2019), 103139. DOI: https://doi.org/10.1016/j.jbi.2019.103139

Dana L Frank, Lamees Khorshid, Jerome F Kiffer, Christine S Moravec, and Michael G McKee. 2010. Biofeedback in medicine: who, when, why and

how? Mental health in family medicine 7, 2 (2010), 85.

Giorgos Giannakakis, Dimitris Grigoriadis, Katerina Giannakaki, Olympia Simantiraki, Alexandros Roniotis, and Manolis Tsiknakis. 2019. Review on psychological stress detection using biosignals. IEEE Transactions on Affective Computing 13, 1 (2019), 440–460. DOI: https://doi.org/10.1109/TAFFC.2019.2927337

Jennifer A Healey and Rosalind W Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on intelligent transportation systems 6, 2 (2005), 156–166. DOI: https://doi.org/10.1109/TITS.2005.848368

Arturo Morales, Franceli L Cibrian, Luis A Castro, and Monica Tentori. 2021. An adaptive model to support biofeedback in AmI environments: a case study in breathing training for autism. Personal and Ubiquitous Computing (2021), 1–16.

Darius Nahavandi, Roohallah Alizadehsani, Abbas Khosravi, and U Rajendra Acharya. 2022. Application of artificial intelligence in wearable devices: Opportunities and challenges. Computer Methods and Programs in Biomedicine 213 (2022), 106541. DOI: https://doi.org/10.1016/j.cmpb.2021.106541

Saptadip Saha, Diptanu Dey, Ie Mei Bhattacharyya, and Anupam Das. 2015. An investigation on biofeedback analysis and psychosomatic applications. In 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE). IEEE, 38–43. DOI: https://doi.org/10.1109/RDCAPE.2015.7281366

Mark S Schwartz and Frank Andrasik. 2017. Biofeedback: A practitioner’s guide. Guilford Publications.

Anselm Strauss and Juliet Corbin. 1990. Basics of qualitative research. Sage publications.

Arturo Morales Téllez, Luis A Castro, and Monica Tentori. 2023. Developing and Evaluating a virtual reality videogame using biofeedback for stress management in sports. Interacting with Computers (2023), iwad025.

Nurdina Widanti, Budi Sumanto, Poppy Rosa, and M Fathur Miftahudin. 2015. Stress level detection using heart rate, blood pressure, and GSR and stress therapy by utilizing infrared. In 2015 International Conference on Industrial Instrumentation and Control (ICIC). Ieee, 275–279 DOI: https://doi.org/10.1109/IIC.2015.7150752

Olson, R. S., & Moore, J. H. 2016, December. TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on automatic machine learning (pp. 66-74). PMLR

Morales Téllez, A., Tentori, M. E., & Castro, L. A. (2021, December). Stress management training in athletes: Design considerations for vr biofeedback systems. In Proceedings of the 8th Mexican Conference on Human-Computer Interaction (pp. 1-3). DOI: https://doi.org/10.1145/3492724.3492730

Downloads

Published

2024-11-30

How to Cite

[1]
Morales, A. et al. 2024. How to design adaptive systems to improve stress management using artificial intelligence . Avances en Interacción Humano-Computadora. 9, 1 (Nov. 2024), 99–103. DOI:https://doi.org/10.47756/aihc.y9i1.153.

Issue

Section

Research Papers

Most read articles by the same author(s)

1 2 > >> 

Similar Articles

1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.