A Convolutional Neural Network Integrating PPG Signal and Extracted Features for Dehydration Classification
Resumo
A desidratação é um sério problema de saúde que pode levar a consequências graves, tornando sua detecção precisa crucial para manter a função corporal adequada. Neste trabalho, nós propomos um modelo de aprendizado de máquina híbrido que pode classificar indivíduos em estados hidratados ou desidratados. Nossa abordagem combina uma rede neural convolucional rasa que extrai recursos locais não supervisionados com características estatísticas de dados de séries temporais obtidos de sensores como Fotopletismografia (PPG) e Atividade Eletrodérmica (EDA). Os resultados mostram que o modelo de classificação proposto alcança uma precisão de 73%, sendo superior à maioria dos trabalhos existentes na literatura que utiliza dados extraídos dos sinais PPG e/ou EDA para classificação de hidratação.Referências
Akar, S. A., Kara, S., Latifoğlu, F., and Bilgic, V. (2013). Spectral analysis of photo-plethysmographic signals: The importance of preprocessing. Biomedical Signal Processing and Control, 8(1):16–22.
Alaslani, R., Perzhilla, L., Rahman, M. M. U., Laleg-Kirati, T.-M., and Al-Naffouri, T. Y. (2024). You can monitor your hydration level using your smartphone camera. arXiv preprint arXiv:2402.07467.
Armstrong, L. E. (2007). Assessing hydration status: the elusive gold standard. Journal of the American College of Nutrition, 26(sup5):575S–584S.
Blagus, R. and Lusa, L. (2013). Smote for high-dimensional class-imbalanced data. BMC bioinformatics, 14:1–16.
Cergolj, V., Stankoski, S., Pirc, M., and Luštrek, M. (2025). Drinking event detection on a sensing wristband using machine learning. Journal of Ambient Intelligence and Smart Environments, 17(2):164–181.
El-Sharkawy, A. M., Sahota, O., and Lobo, D. N. (2015). Acute and chronic effects of hydration status on health. Nutrition reviews, 73(suppl 2):97–109.
Gomes, D. and Sousa, I. (2019). Real-time drink trigger detection in free-living conditions using inertial sensors. Sensors, 19(9):2145.
Islam, T., Rigan, M. M. H., Bhuiyan, M. O. H., Hashem, T., and Rahman, M. M. (2025). H2opulse: Smartphone-assisted vein evaluation for early recognition of dehydration. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(2):1–24.
Kulkarni, N., Compton, C., Luna, J., and Alam, M. A. U. (2021). A non-invasive context-aware dehydration alert system. In Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, pages 157–159.
Lee, J. W. and Kim, Y. (2024). Association of plain water intake with self-reported depression and suicidality among korean adolescents. Epidemiology and health, 46:e2024019.
Li, S., Xiao, X., and Zhang, X. (2024). Association between plain water intake and risk of hypertension: longitudinal analyses from the china health and nutrition survey. Frontiers in Public Health, 11:1280653.
Liaqat, S., Dashtipour, K., Arshad, K., and Ramzan, N. (2020). Non invasive skin hydration level detection using machine learning. Electronics, 9(7):1086.
Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A., Arshad, K., Assaleh, K., and Ramzan, N. (2022). Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Scientific Reports, 12(1):3715.
Ortega Anderez, D., Lotfi, A., and Pourabdollah, A. (2021). A deep learning based wearable system for food and drink intake recognition. Journal of Ambient Intelligence and Humanized Computing, 12:9435–9447.
Plecher, D. A., Eichhorn, C., Lurz, M., Leipold, N., Böhm, M., Krcmar, H., Ott, A., Volkert, D., and Klinker, G. (2019). Interactive drinking gadget for the elderly and alzheimer patients. In Human Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments: 5th International Conference, ITAP 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part II 21, pages 444–463. Springer.
Popkin, B. M., D’Anci, K. E., and Rosenberg, I. H. (2010). Water, hydration, and health. Nutrition reviews, 68(8):439–458.
Posada-Quintero, H. F., Reljin, N., Moutran, A., Georgopalis, D., Lee, E. C.-H., Giersch, G. E., Casa, D. J., and Chon, K. H. (2019). Mild dehydration identification using machine learning to assess autonomic responses to cognitive stress. Nutrients, 12(1):42.
Reljin, N., Malyuta, Y., Zimmer, G., Mendelson, Y., Blehar, D. J., Darling, C. E., and Chon, K. H. (2018). Automatic detection of dehydration using support vector machines. In 2018 14th Symposium on Neural Networks and Applications (NEUREL), pages 1–6. IEEE.
Rizwan, A., Ali, N. A., Zoha, A., Ozturk, M., Alomainy, A., Imran, M. A., and Abbasi, Q. H. (2020). Non-invasive hydration level estimation in human body using galvanic skin response. IEEE Sensors Journal, 20(9):4891–4900.
Sabry, F., Eltaras, T., Labda, W., Hamza, F., Alzoubi, K., and Malluhi, Q. (2022). Towards on-device dehydration monitoring using machine learning from wearable device’s data. Sensors, 22(5):1887.
Siyoucef, S., Adnane, M., Rahman, M. M. U., Laleg-Kirati, T.-M., and Al-Naffouri, T. Y. (2025). Non-invasive monitoring of dehydration of fasting and sportspeople subjects via skin capacitance. IEEE Sensors journal.
Theodoridis, X., Poulia, K. A., and Chourdakis, M. (2025). What’s new about hydration in dementia? Current Opinion in Clinical Nutrition & Metabolic Care, 28(1):20–24.
Van Gent, P., Farah, H., Van Nes, N., and Van Arem, B. (2019). Heartpy: A novel heart rate algorithm for the analysis of noisy signals. Transportation research part F: traffic psychology and behaviour, 66:368–378.
van Iterson, H. C., Liang, R.-H., and Markopoulos, P. (2025). Redesigning fluid tracking probes for elderly lifestyle retrofit. In Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, pages 1–6.
Alaslani, R., Perzhilla, L., Rahman, M. M. U., Laleg-Kirati, T.-M., and Al-Naffouri, T. Y. (2024). You can monitor your hydration level using your smartphone camera. arXiv preprint arXiv:2402.07467.
Armstrong, L. E. (2007). Assessing hydration status: the elusive gold standard. Journal of the American College of Nutrition, 26(sup5):575S–584S.
Blagus, R. and Lusa, L. (2013). Smote for high-dimensional class-imbalanced data. BMC bioinformatics, 14:1–16.
Cergolj, V., Stankoski, S., Pirc, M., and Luštrek, M. (2025). Drinking event detection on a sensing wristband using machine learning. Journal of Ambient Intelligence and Smart Environments, 17(2):164–181.
El-Sharkawy, A. M., Sahota, O., and Lobo, D. N. (2015). Acute and chronic effects of hydration status on health. Nutrition reviews, 73(suppl 2):97–109.
Gomes, D. and Sousa, I. (2019). Real-time drink trigger detection in free-living conditions using inertial sensors. Sensors, 19(9):2145.
Islam, T., Rigan, M. M. H., Bhuiyan, M. O. H., Hashem, T., and Rahman, M. M. (2025). H2opulse: Smartphone-assisted vein evaluation for early recognition of dehydration. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(2):1–24.
Kulkarni, N., Compton, C., Luna, J., and Alam, M. A. U. (2021). A non-invasive context-aware dehydration alert system. In Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, pages 157–159.
Lee, J. W. and Kim, Y. (2024). Association of plain water intake with self-reported depression and suicidality among korean adolescents. Epidemiology and health, 46:e2024019.
Li, S., Xiao, X., and Zhang, X. (2024). Association between plain water intake and risk of hypertension: longitudinal analyses from the china health and nutrition survey. Frontiers in Public Health, 11:1280653.
Liaqat, S., Dashtipour, K., Arshad, K., and Ramzan, N. (2020). Non invasive skin hydration level detection using machine learning. Electronics, 9(7):1086.
Liaqat, S., Dashtipour, K., Rizwan, A., Usman, M., Shah, S. A., Arshad, K., Assaleh, K., and Ramzan, N. (2022). Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Scientific Reports, 12(1):3715.
Ortega Anderez, D., Lotfi, A., and Pourabdollah, A. (2021). A deep learning based wearable system for food and drink intake recognition. Journal of Ambient Intelligence and Humanized Computing, 12:9435–9447.
Plecher, D. A., Eichhorn, C., Lurz, M., Leipold, N., Böhm, M., Krcmar, H., Ott, A., Volkert, D., and Klinker, G. (2019). Interactive drinking gadget for the elderly and alzheimer patients. In Human Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments: 5th International Conference, ITAP 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part II 21, pages 444–463. Springer.
Popkin, B. M., D’Anci, K. E., and Rosenberg, I. H. (2010). Water, hydration, and health. Nutrition reviews, 68(8):439–458.
Posada-Quintero, H. F., Reljin, N., Moutran, A., Georgopalis, D., Lee, E. C.-H., Giersch, G. E., Casa, D. J., and Chon, K. H. (2019). Mild dehydration identification using machine learning to assess autonomic responses to cognitive stress. Nutrients, 12(1):42.
Reljin, N., Malyuta, Y., Zimmer, G., Mendelson, Y., Blehar, D. J., Darling, C. E., and Chon, K. H. (2018). Automatic detection of dehydration using support vector machines. In 2018 14th Symposium on Neural Networks and Applications (NEUREL), pages 1–6. IEEE.
Rizwan, A., Ali, N. A., Zoha, A., Ozturk, M., Alomainy, A., Imran, M. A., and Abbasi, Q. H. (2020). Non-invasive hydration level estimation in human body using galvanic skin response. IEEE Sensors Journal, 20(9):4891–4900.
Sabry, F., Eltaras, T., Labda, W., Hamza, F., Alzoubi, K., and Malluhi, Q. (2022). Towards on-device dehydration monitoring using machine learning from wearable device’s data. Sensors, 22(5):1887.
Siyoucef, S., Adnane, M., Rahman, M. M. U., Laleg-Kirati, T.-M., and Al-Naffouri, T. Y. (2025). Non-invasive monitoring of dehydration of fasting and sportspeople subjects via skin capacitance. IEEE Sensors journal.
Theodoridis, X., Poulia, K. A., and Chourdakis, M. (2025). What’s new about hydration in dementia? Current Opinion in Clinical Nutrition & Metabolic Care, 28(1):20–24.
Van Gent, P., Farah, H., Van Nes, N., and Van Arem, B. (2019). Heartpy: A novel heart rate algorithm for the analysis of noisy signals. Transportation research part F: traffic psychology and behaviour, 66:368–378.
van Iterson, H. C., Liang, R.-H., and Markopoulos, P. (2025). Redesigning fluid tracking probes for elderly lifestyle retrofit. In Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, pages 1–6.
Publicado
29/09/2025
Como Citar
RODRIGUES, José Mateus Cordova; LEMES, Ayrton Finicelli; UTYIAMA, Daniel Mitsuaki da Silva; GOHL, Pedro Daniel da Silva; SOUTO, Eduardo James Pereira; GIUSTI, Rafael.
A Convolutional Neural Network Integrating PPG Signal and Extracted Features for Dehydration Classification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1245-1256.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14491.
