Identificação e Análise de Dias Depressivos a Partir da Classificação em Séries Temporais de Atividade Motora
Resumo
Este estudo investiga o uso de diferentes classificadores de séries temporais para a identificação de dias depressivos por meio da análise da atividade motora diária de indivíduos com e sem diagnóstico clínico da doença. Os resultados mostram uma melhora significativa nos algoritmos de séries temporais em comparação com os modelos tabulares de referência. No entanto, detectar com precisão os dias de depressão continua sendo um desafio. A análise qualitativa de erros indica que as falhas são consistentes entre diferentes famílias de algoritmos e estão associadas a perfis circadianos específicos influenciados por fatores externos. Ainda assim, a técnica se mostra promissora como base para o monitoramento longitudinal, apoiando o desenvolvimento de políticas de saúde pública eficazes em sistemas como o Sistema Único de Saúde (SUS) brasileiro e complementando o trabalho de profissionais especializados.Referências
American Psychiatric Association, D., American Psychiatric Association, D., et al. (2013). Diagnostic and statistical manual of mental disorders: DSM-5, volume 5. American Psychiatric Association.
Aminifar, A., Rabbi, F., Pun, V. K. I., and Lamo, Y. (2021). Monitoring motor activity data for detecting patients’ depression using data augmentation and privacy-preserving distributed learning. In Annual Int’l Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2163–2169.
Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh, E. (2017). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and Knowledge Discovery, 31(3):606–660.
Benedito-Silva, A. A., Gonçalves, B., Vartanian, D., Santos, E. H. R., Miguel, M. A. L., Pedrazzoli, M., and Gonçalves, B. (2021). Especificações técnicas e recomendações sobre o uso da actigrafia. Consenso Brasileiro de Actigrafia. São Paulo: Segmento Farma Editores, pages 13–28.
Benoit, J., Onyeaka, H., Keshavan, M., and Torous, J. (2020). Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses. Harvard review of psychiatry, 28(5):296–304.
Bril-Barniv, S., Moran, G. S., Naaman, A., Roe, D., and Karnieli-Miller, O. (2017). A qualitative study examining experiences and dilemmas in concealment and disclosure of people living with serious mental illness. Qualitative Health Research, 27(4):573–583.
Buyukdura, J. S., McClintock, S. M., and Croarkin, P. E. (2011). Psychomotor retardation in depression: biological underpinnings, measurement, and treatment. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 35(2):395–409.
Espino-Salinas, C. H., Galván-Tejada, C. E., Luna-García, H., Gamboa-Rosales, H., Celaya-Padilla, J. M., Zanella-Calzada, L. A., and Tejada, J. I. G. (2022). Two-dimensional convolutional neural network for depression episodes detection in real time using motor activity time series of depresjon dataset. Bioengineering, 9(9):458.
Garcia-Ceja, E., Riegler, M., Jakobsen, P., Tørresen, J., Nordgreen, T., Oedegaard, K. J., and Fasmer, O. B. (2018a). Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In Proc.of the ACM Multimedia Systems Conference, pages 472–477.
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., and Tørresen, J. (2018b). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing, 51:1–26.
Middlehurst, M., Schäfer, P., and Bagnall, A. (2024). Bake off redux: a review and experimental evaluation of recent time series classification algorithms: M. middlehurst et al. Data Mining and Knowledge Discovery, 38(4):1958–2031.
Song, S., Huo, Y., Tang, S., Cheong, J., Gao, R., Valstar, M., and Gunes, H. (2025). Automatic depression assessment using machine learning: A comprehensive survey. arXiv preprint arXiv:2506.18915.
Tazawa, Y., Wada, M., Mitsukura, Y., Takamiya, A., Kitazawa, M., Yoshimura, M., Mimura, M., and Kishimoto, T. (2019). Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis. Journal of Affective Disorders, 253:257–269.
World Health Organization (2017). Depression and other common mental disorders: global health estimates. Geneva. Licence: CC BY-NC-SA 3.0 IGO.
World Health Organization (2022). World mental health report: Transforming mental health for all. Technical report, World Health Organization, Geneva.
Zhou, Z.-H. (2025). Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.
Aminifar, A., Rabbi, F., Pun, V. K. I., and Lamo, Y. (2021). Monitoring motor activity data for detecting patients’ depression using data augmentation and privacy-preserving distributed learning. In Annual Int’l Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 2163–2169.
Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh, E. (2017). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and Knowledge Discovery, 31(3):606–660.
Benedito-Silva, A. A., Gonçalves, B., Vartanian, D., Santos, E. H. R., Miguel, M. A. L., Pedrazzoli, M., and Gonçalves, B. (2021). Especificações técnicas e recomendações sobre o uso da actigrafia. Consenso Brasileiro de Actigrafia. São Paulo: Segmento Farma Editores, pages 13–28.
Benoit, J., Onyeaka, H., Keshavan, M., and Torous, J. (2020). Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses. Harvard review of psychiatry, 28(5):296–304.
Bril-Barniv, S., Moran, G. S., Naaman, A., Roe, D., and Karnieli-Miller, O. (2017). A qualitative study examining experiences and dilemmas in concealment and disclosure of people living with serious mental illness. Qualitative Health Research, 27(4):573–583.
Buyukdura, J. S., McClintock, S. M., and Croarkin, P. E. (2011). Psychomotor retardation in depression: biological underpinnings, measurement, and treatment. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 35(2):395–409.
Espino-Salinas, C. H., Galván-Tejada, C. E., Luna-García, H., Gamboa-Rosales, H., Celaya-Padilla, J. M., Zanella-Calzada, L. A., and Tejada, J. I. G. (2022). Two-dimensional convolutional neural network for depression episodes detection in real time using motor activity time series of depresjon dataset. Bioengineering, 9(9):458.
Garcia-Ceja, E., Riegler, M., Jakobsen, P., Tørresen, J., Nordgreen, T., Oedegaard, K. J., and Fasmer, O. B. (2018a). Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In Proc.of the ACM Multimedia Systems Conference, pages 472–477.
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., and Tørresen, J. (2018b). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing, 51:1–26.
Middlehurst, M., Schäfer, P., and Bagnall, A. (2024). Bake off redux: a review and experimental evaluation of recent time series classification algorithms: M. middlehurst et al. Data Mining and Knowledge Discovery, 38(4):1958–2031.
Song, S., Huo, Y., Tang, S., Cheong, J., Gao, R., Valstar, M., and Gunes, H. (2025). Automatic depression assessment using machine learning: A comprehensive survey. arXiv preprint arXiv:2506.18915.
Tazawa, Y., Wada, M., Mitsukura, Y., Takamiya, A., Kitazawa, M., Yoshimura, M., Mimura, M., and Kishimoto, T. (2019). Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis. Journal of Affective Disorders, 253:257–269.
World Health Organization (2017). Depression and other common mental disorders: global health estimates. Geneva. Licence: CC BY-NC-SA 3.0 IGO.
World Health Organization (2022). World mental health report: Transforming mental health for all. Technical report, World Health Organization, Geneva.
Zhou, Z.-H. (2025). Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.
Publicado
01/06/2026
Como Citar
FIGUEIREDO, Guilherme A. Rocha de; REIS, Julio C. S..
Identificação e Análise de Dias Depressivos a Partir da Classificação em Séries Temporais de Atividade Motora. In: WORKSHOP ON MINING DIGITAL AND SOCIAL SIGNALS FOR PUBLIC HEALTH - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 393-402.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2026.25439.
