Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices

Pires, Ivan M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, M.C.C.

http://hdl.handle.net/10400.11/9011

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Authors

Pires, Ivan M.

Pombo, Nuno

Flórez-Revuelta, Francisco

Spinsante, Susanna

Teixeira, M.C.C.

Abstract(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.

Keywords

Mobile devices sensors Sensor data fusion Artificial neural networks Identification of activities of daily living

URI

http://hdl.handle.net/10400.11/9011

Citation

PIRES, Ivan M. [et al.] (2018) - Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices. Pervasive and Mobile Computing. 47, p. 78-93. DOI: https://doi.org/10.1016/j.pmcj.2018.05.005