Análise de Históricos de Contextos para Classificação do Estresse Mental em Situações Reais por meio da Variabilidade da Frequência Cardı́aca
Resumo
O estresse mental é um dos principais causadores de problemas na saúde fı́sica e emocional. Desta forma, torna-se estratégico mensurar o estado de estresse diário por meio fisiológico a fim de utilizá-lo em aplicações que visem o bem-estar do indivı́duo. Entretanto, as movimentações diárias influenciam na coleta de dados fisiológicos, causando ruı́do. Este trabalho coleta e analisa os históricos de contextos de 5 indivı́duos com o intuito da classificação do estresse mental por meio da variabilidade da frequência cardı́aca. Como contribuição cientı́fica, o método visa identificar e remover ruı́do bem como mostrar uma relação entre os nı́veis de estresse e a localização em que foram mensurados. A melhor performance obtida, dentre três algoritmos de aprendizado de máquina utilizados foi do classificador Support Vector Machine, resultando em 82% de acurácia.
Palavras-chave:
Classification, Mental Stress, Heart Rate
Referências
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Fink, G. (2017). Stress: Concepts, Definition and History. Reference Module in Neuros- cience and Biobehavioral Psychology, (January):0–9.
Gjoreski, M., Luštrek, M., Gams, M., and Gjoreski, H. (2017). Monitoring stress with a wrist device using context. Journal of Biomedical Informatics, 73:159–170.
Gravenhorst, F., Muaremi, A., Bardram, J., Grünerbl, A., Mayora, O., Wurzer, G., Frost, M., Osmani, V., Arnrich, B., Lukowicz, P., and Tröster, G. (2015). Mobile phones as medical devices in mental disorder treatment: an overview. Personal and Ubiquitous Computing, 19(2):335–353.
Kursa, M B. and Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11):1–13.
Kusserow, M., Amft, O., and Tröster, G. (2013). Monitoring stress arousal in the wild. IEEE Pervasive Computing, 12(2):28–37.
Mishra, V., Hao, T., Sun, S., Walter, K. N., Ball, M. J., Chen, C.-H., and Zhu, X. (2018). Investigating the Role of Context in Perceived Stress Detection in the Wild. In Ubi- Comp ’18, pages 1708–1716, New York, New York, USA. ACM Press.
Peltola, M. (2012). Role of editing of r-r intervals in the analysis of heart rate variability. Frontiers in Physiology, 3:148.
Pereira, T., Almeida, P. R., Cunha, J. P., and Aguiar, A. (2017). Heart rate variability metrics for fine-grained stress level assessment. Computer Methods and Programs in Biomedicine, 148:71–80.
Peternel, K., Poga, Tavčar, R., and Kos, A. (2012). A presence-based context-aware chronic stress recognition system. Sensors (Switzerland), 12(11):15888–15906.
Quintana, D. S., Alvares, G. A., and Heathers, J. A. J. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (graph): recommendations to advance research communication. Translational Psychiatry, 6(5):e803–e803.
Shiffman, S., Stone, A. A., and Hufford, M. R. (2008). Ecological Momentary Assess- ment. Annual Review of Clinical Psychology, 4(1):1–32.
Barbosa, J. L. V. (2015). Ubiquitous computing: Applications and research opportunities. In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–8. IEEE.
Cohen, S., Janicki-Deverts, D., Doyle, W. J., Miller, G. E., Frank, E., Rabin, B. S., and Turner, R. B. (2012). Chronic stress, glucocorticoid receptor resistance, inflammation, and disease risk. Proceedings of the National Academy of Sciences, 109(16):5995– 5999.
da Rosa, J. H., Barbosa, J. L., and Ribeiro, G. D. (2016). ORACON: An adaptive model for context prediction. Expert Systems with Applications, 45:56–70.
Feher, J. (2012). Autonomic Nervous System. In Quantitative Human Physiology, pages 403–416. Elsevier.
Fink, G. (2017). Stress: Concepts, Definition and History. Reference Module in Neuros- cience and Biobehavioral Psychology, (January):0–9.
Gjoreski, M., Luštrek, M., Gams, M., and Gjoreski, H. (2017). Monitoring stress with a wrist device using context. Journal of Biomedical Informatics, 73:159–170.
Gravenhorst, F., Muaremi, A., Bardram, J., Grünerbl, A., Mayora, O., Wurzer, G., Frost, M., Osmani, V., Arnrich, B., Lukowicz, P., and Tröster, G. (2015). Mobile phones as medical devices in mental disorder treatment: an overview. Personal and Ubiquitous Computing, 19(2):335–353.
Kursa, M B. and Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11):1–13.
Kusserow, M., Amft, O., and Tröster, G. (2013). Monitoring stress arousal in the wild. IEEE Pervasive Computing, 12(2):28–37.
Mishra, V., Hao, T., Sun, S., Walter, K. N., Ball, M. J., Chen, C.-H., and Zhu, X. (2018). Investigating the Role of Context in Perceived Stress Detection in the Wild. In Ubi- Comp ’18, pages 1708–1716, New York, New York, USA. ACM Press.
Peltola, M. (2012). Role of editing of r-r intervals in the analysis of heart rate variability. Frontiers in Physiology, 3:148.
Pereira, T., Almeida, P. R., Cunha, J. P., and Aguiar, A. (2017). Heart rate variability metrics for fine-grained stress level assessment. Computer Methods and Programs in Biomedicine, 148:71–80.
Peternel, K., Poga, Tavčar, R., and Kos, A. (2012). A presence-based context-aware chronic stress recognition system. Sensors (Switzerland), 12(11):15888–15906.
Quintana, D. S., Alvares, G. A., and Heathers, J. A. J. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (graph): recommendations to advance research communication. Translational Psychiatry, 6(5):e803–e803.
Shiffman, S., Stone, A. A., and Hufford, M. R. (2008). Ecological Momentary Assess- ment. Annual Review of Clinical Psychology, 4(1):1–32.
Publicado
12/07/2019
Como Citar
BAVARESCO, Rodrigo; BARBOSA, Jorge .
Análise de Históricos de Contextos para Classificação do Estresse Mental em Situações Reais por meio da Variabilidade da Frequência Cardı́aca. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 11. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2019.6586.
