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Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales

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Machine Learning, Optimization, and Data Science (LOD 2021)


The subject of physical activity is becoming prominent in the healthcare system for the improvement and monitoring of movement disabilities. Existing algorithms are effective in detecting changes in data streams but most of these approaches are not focused on measuring the change intensity and duration. In this paper, we improve on the geometric moving average martingale method by optimising the parameters in the weighted average using a genetic algorithm. The proposed approach enables us to estimate the intensity and duration of transitions that happen in human activity recognition scenarios. Results show that the proposed method makes some improvement over previous martingale techniques.

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This work was supported by a University of Ulster Vice-Chancellor’s Research Studentship. The authors would like to thank anonymous reviewers for their constructive suggestions.

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Correspondence to Jonathan Etumusei .

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Etumusei, J., Carracedo, J.M., McClean, S. (2022). Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham.

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  • Print ISBN: 978-3-030-95466-6

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