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Orientador(es)
Resumo(s)
Several types of sensors have been available in off‐the‐shelf mobile devices, including motion,
magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired
from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the
recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system
developed in this study includes data acquisition, data processing, data fusion, and classification
methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and
evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This
novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it
suitability and reliability for the ADL recognition.
Descrição
Palavras-chave
Mobile devices sensors Sensor data fusion Artificial neural networks Identification of activities of daily living
Contexto Educativo
Citação
I.M. Pires, N.M. Garcia, N. Pombo, F. Florez-Revuelta, S. Spinsante, M.C. Teixeira, Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices, Pervasive and Mobile Computing (2018), https://doi.org/10.1016/j.pmcj.2018.05.005