PCare: Um Modelo para Assistência Postural em Ambientes Inteligentes
Resumo
Este artigo apresenta o modelo PCare para assistência postural em ambientes inteligentes. A principal contribuição científica deste trabalho é a assistência postural através da detecção da postura incorreta e o armazenamento das informações de contexto. Essa abordagem permite identificar padrões associados a hábitos posturais inadequados e apoiar decisões clínicas baseadas na rotina do usuário. Um protótipo foi desenvolvido com sensores, ESP32 e um aplicativo em Flutter. A avaliação foi conduzida com sete profissionais de saúde por meio do Technology Acceptance Model. Os resultados indicaram 69% de concordância quanto à facilidade de uso e 71% quanto à utilidade percebida.
Referências
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Dupont, D., Barbosa, J. L. V. and Alves, B. M. (2020). CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories. Pattern Analysis and Applications, v. 23, n. 2, p. 725–734.
Flutur, G., Bogdan Movileanu, Karoly, L., et al. (2019). Smart Chair System for Posture Correction. Euromicro Conference on Digital System Design (DSD), v. 22, p. 436–441.
Guevara, C., Janio Jadán-Guerrero, Bonilla-Jurado, D., et al. (2019). Fuzzy Model for Back Posture Correction During the Walk. Advances in intelligent systems and computing, v. 959, p. 299–305.
Martini, B. G., Helfer, G. A., Barbosa, J. L. V., et al. (2021). IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories. Sensors, v. 21, n. 5, p. 1631.
Odesola, D. F., Kulon, J., Verghese, S., Partlow, A. and Gibson, C. (2025). A Smart System for Continuous Sitting Posture Monitoring, Assessment, and Personalized Feedback. Sensors, v. 25, n. 18, p. 5610.
Pereira, A. P., Machado Neto, O. J., Elui, V. M. C. and Pimentel, M. da G. C. (2025). Wearable Smartphone-Based Multisensory Feedback System for Torso Posture Correction: Iterative Design and Within-Subjects Study. JMIR Aging, v. 8, p. e55455.
Piñero-Fuentes, E., Canas-Moreno, S., Rios-Navarro, A., et al. (2021). A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders. Sensors, v. 21, n. 15, p. 5236.
Ribeiro, P., Soares, A. R., Girão, R., Neto, M. and Cardoso, S. (2020). Spine Cop: Posture Correction Monitor and Assistant. Sensors, v. 20, n. 18, p. 5376.
Roh, J., Park, H., Lee, K., et al. (2021). Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning. Sensors, v. 18, n. 2, p. 208.
Santana, Bradachi, G. A., Eduarda, M., et al. (2025). OD4CoT: um dataset baseado em ontologia para o Contexto das Coisas. Anais do XVII Simpósio Brasileiro de Computação Ubíqua e Pervasiva, p. 141–150.
SAP Help Portal | SAP Online Help (2025). [link]. Acesso em: 28 out. 2025
Tavares, J., Barbosa, J. L. V., Cardoso, I., et al. (2015). Hefestos: an intelligent system applied to ubiquitous accessibility. Universal Access in the Information Society, v. 15, n. 4, p. 589–607.
Zhao, S. and Su, Y. (2024). Sitting Posture Recognition Based on the Computer’s Camera. Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition, p. 1–5.
Anwary, A. R., Cetinkaya, D., Vassallo, M. and Bouchachia, H. (2020). Smart-Cover: A real time sitting posture monitoring system. Sensors and Actuators A: Physical, v. 317, p. 112451.
Aranda, J., Bavaresco, R., Varella , J., Yamin, A. and Barbosa, J. (2021). A computational model for adaptive recording of vital signs through context histories. Journal of Ambient Intelligence and Humanized Computing, v. 1, p. 1–15.
Bavaresco, R., Ren, Y., Barbosa, J. and Li, G. P. (2024). An ontology-based framework for worker’s health reasoning enabled by machine learning. Computers & Industrial Engineering, v. 193, p. 110310–110310.
Bourahmoune, K., Ishac, K. and Amagasa, T. (2022). Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion. Sensors, v. 22, n. 14, p. 5337.7
Camboim, B. D., Tavares, R., Tavares, M. C. and Barbosa, J. L. V. (2023). Posture monitoring in healthcare: a systematic mapping study and taxonomy. Medical & biological engineering & computing, v. 61, n. 8, p. 1887–1899.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, v. 13, n. 3, p. 319–340.
Difini, G. M., Martins, M. G. and Barbosa, J. L. V. (2021). Human Pose Estimation for Training Assistance: a Systematic Literature Review. Brazilian Symposium on Multimedia and the Web, p. 189–196.
Dupont, D., Barbosa, J. L. V. and Alves, B. M. (2020). CHSPAM: a multi-domain model for sequential pattern discovery and monitoring in contexts histories. Pattern Analysis and Applications, v. 23, n. 2, p. 725–734.
Flutur, G., Bogdan Movileanu, Karoly, L., et al. (2019). Smart Chair System for Posture Correction. Euromicro Conference on Digital System Design (DSD), v. 22, p. 436–441.
Guevara, C., Janio Jadán-Guerrero, Bonilla-Jurado, D., et al. (2019). Fuzzy Model for Back Posture Correction During the Walk. Advances in intelligent systems and computing, v. 959, p. 299–305.
Martini, B. G., Helfer, G. A., Barbosa, J. L. V., et al. (2021). IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories. Sensors, v. 21, n. 5, p. 1631.
Odesola, D. F., Kulon, J., Verghese, S., Partlow, A. and Gibson, C. (2025). A Smart System for Continuous Sitting Posture Monitoring, Assessment, and Personalized Feedback. Sensors, v. 25, n. 18, p. 5610.
Pereira, A. P., Machado Neto, O. J., Elui, V. M. C. and Pimentel, M. da G. C. (2025). Wearable Smartphone-Based Multisensory Feedback System for Torso Posture Correction: Iterative Design and Within-Subjects Study. JMIR Aging, v. 8, p. e55455.
Piñero-Fuentes, E., Canas-Moreno, S., Rios-Navarro, A., et al. (2021). A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders. Sensors, v. 21, n. 15, p. 5236.
Ribeiro, P., Soares, A. R., Girão, R., Neto, M. and Cardoso, S. (2020). Spine Cop: Posture Correction Monitor and Assistant. Sensors, v. 20, n. 18, p. 5376.
Roh, J., Park, H., Lee, K., et al. (2021). Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning. Sensors, v. 18, n. 2, p. 208.
Santana, Bradachi, G. A., Eduarda, M., et al. (2025). OD4CoT: um dataset baseado em ontologia para o Contexto das Coisas. Anais do XVII Simpósio Brasileiro de Computação Ubíqua e Pervasiva, p. 141–150.
SAP Help Portal | SAP Online Help (2025). [link]. Acesso em: 28 out. 2025
Tavares, J., Barbosa, J. L. V., Cardoso, I., et al. (2015). Hefestos: an intelligent system applied to ubiquitous accessibility. Universal Access in the Information Society, v. 15, n. 4, p. 589–607.
Zhao, S. and Su, Y. (2024). Sitting Posture Recognition Based on the Computer’s Camera. Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition, p. 1–5.
Publicado
19/07/2026
Como Citar
CAMBOIM, Bruno Dahmer; BECKER, Maitê Débora Lamb; BARBOSA, Débora Nice Ferrari; BARBOSA, Jorge Luis Victória.
PCare: Um Modelo para Assistência Postural em Ambientes Inteligentes. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 18. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 350-360.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2026.20628.
