Modeling complex human activities with inertial units, ambient sensors, and machine learning
Resumo
O reconhecimento de atividades humanas é essencial para aplicações em saúde, esportes e ambientes inteligentes. Trabalhos recentes utilizam desde modelos clássicos até redes neurais profundas. Contudo, poucos exploram a influência da similaridade entre atividades complexas na acurácia dos modelos. Este trabalho compara árvores de decisão, florestas aleatórias e uma rede neural convolucional unidimensional (CNN-1D) em dois conjuntos de dados públicos: PAMAP2 e HWU-USP. No PAMAP2, a CNN-1D mostrou-se mais robusta (acurácia de 62,48% ± 10,31%), enquanto no HWU-USP o modelo Floresta Aleatória foi mais estável (acurácia de 38,93% ± 3,94%), destacando a importância do ajuste de hiperparâmetros para tarefas complexas.Referências
Alagoz, C. (2024). Comparative analysis of xgboost and minirocket algortihms for human activity recognition. arXiv preprint arXiv:2402.18296.
Cicirelli, G., Marani, R., Petitti, A., Milella, A., and D’Orazio, T. (2021). Ambient assisted living: A review of technologies, methodologies and future perspectives for healthy aging of population. Sensors, 21(10).
Dempster, A., Schmidt, D. F., and Webb, G. I. (2021). Minirocket: A very fast (almost) deterministic transform for time series classification. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 248–257.
D’Arco, L., Wang, H., and Zheng, H. (2022). Assessing impact of sensors and feature selection in smart-insole-based human activity recognition. Methods and Protocols, 5(3):45.
Guo, W., Yamagishi, S., and Jing, L. (2024). Human activity recognition via wi-fi and inertial sensors with machine learning. IEEE Access, 12:18821–18836.
Jovanovic, M., Mitrov, G., Zdravevski, E., Lameski, P., Colantonio, S., Kampel, M., Tellioglu, H., and Florez-Revuelta, F. (2022). Ambient assisted living: Scoping review of artificial intelligence models, domains, technology, and concerns. J Med Internet Res, 24(11):e36553.
Manouchehri, N. and Bouguila, N. (2023). Human activity recognition with an hmm-based generative model. Sensors, 23(3):1390.
Marques, G., Bhoi, A. K., Albuquerque, V., and S., H. (2021). IoT in Healthcare and Ambient Assisted Living. Springer.
O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al. (2019). Keras Tuner. [link].
Ranieri, C. M., MacLeod, S., Dragone, M., Vargas, P. A., and Romero, R. A. F. (2021). Activity recognition for ambient assisted living with videos, inertial units and ambient sensors. Sensors, 21(3):768.
Reiss, A. and Stricker, D. (2012). Introducing a new benchmarked dataset for activity monitoring. In 2012 16th international symposium on wearable computers, pages 108–109. IEEE.
Yadav, S. K., Tiwari, K., Pandey, H. M., and Akbar, S. A. (2021). A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions. Knowledge-Based Systems, 223:106970.
Cicirelli, G., Marani, R., Petitti, A., Milella, A., and D’Orazio, T. (2021). Ambient assisted living: A review of technologies, methodologies and future perspectives for healthy aging of population. Sensors, 21(10).
Dempster, A., Schmidt, D. F., and Webb, G. I. (2021). Minirocket: A very fast (almost) deterministic transform for time series classification. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 248–257.
D’Arco, L., Wang, H., and Zheng, H. (2022). Assessing impact of sensors and feature selection in smart-insole-based human activity recognition. Methods and Protocols, 5(3):45.
Guo, W., Yamagishi, S., and Jing, L. (2024). Human activity recognition via wi-fi and inertial sensors with machine learning. IEEE Access, 12:18821–18836.
Jovanovic, M., Mitrov, G., Zdravevski, E., Lameski, P., Colantonio, S., Kampel, M., Tellioglu, H., and Florez-Revuelta, F. (2022). Ambient assisted living: Scoping review of artificial intelligence models, domains, technology, and concerns. J Med Internet Res, 24(11):e36553.
Manouchehri, N. and Bouguila, N. (2023). Human activity recognition with an hmm-based generative model. Sensors, 23(3):1390.
Marques, G., Bhoi, A. K., Albuquerque, V., and S., H. (2021). IoT in Healthcare and Ambient Assisted Living. Springer.
O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al. (2019). Keras Tuner. [link].
Ranieri, C. M., MacLeod, S., Dragone, M., Vargas, P. A., and Romero, R. A. F. (2021). Activity recognition for ambient assisted living with videos, inertial units and ambient sensors. Sensors, 21(3):768.
Reiss, A. and Stricker, D. (2012). Introducing a new benchmarked dataset for activity monitoring. In 2012 16th international symposium on wearable computers, pages 108–109. IEEE.
Yadav, S. K., Tiwari, K., Pandey, H. M., and Akbar, S. A. (2021). A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions. Knowledge-Based Systems, 223:106970.
Publicado
29/09/2025
Como Citar
COLOMBO, Laura Sthefany; CONDE, André Luiz da Silva; PEREIRA, Leonardo Tórtoro; RANIERI, Caetano Mazzoni.
Modeling complex human activities with inertial units, ambient sensors, and machine learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 2056-2067.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14517.
