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

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.

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.

Authors

  • Martin Gjoreski Department of Intelligent Systems, Jozef Stefan Institute

DOI:

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

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Published

03/15/2021

How to Cite

Gjoreski, M. (2021). A Method for Combining Classical and Deep Machine Learning for Mobile Health and Behavior Monitoring. Informatica, 45(1). https://doi.org/10.31449/inf.v45i1.3482

Issue

Vol. 45 No. 1 (2021)

Section

Thesis summary

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