A Method for Combining Classical and Deep Machine Learning for Mobile Health and Behavior Monitoring

Martin Gjoreski

Abstract


This paper summarizes the doctoral dissertation of the author, which presents a method for fusing classical and deep machine learning for mobile health and behavior monitoring with wearable sensors.


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References


M. Gjoreski, A fusion of classical and deep machine learning for mobile health and behavior monitoring with wearable sensors, PhD Thesis, IPS Jožef Stefan, Ljubljana, Slovenia, 2020.

M. Gjoreski, A. Gradišek, B. Budna, M. Gams, and G. Poglajen, “Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds,” IEEE Access, vol. 8, pp. 20313–20324, 2020.

M. Gjoreski, M. Gams, M. Luštrek, P. Genc, J.-U. Garbas, and T. Hassan, “Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals,” IEEE Access, vol. 8, pp. 70590–70603, 2020.

M. Gjoreski, V. Janko, G. Slapničar, M. Mlakar, N. Reščič, J. Bizjak, V. Drobnič, M. Marinko, N. Mlakar, M. Luštrek, and M. Gams, “Classical, and deep learning methods for recognizing human activities, and modes of transportation with smartphone sensors,” Information Fusion, vol. 62, pp. 47–62, 2020.




DOI: https://doi.org/10.31449/inf.v45i1.3482

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