Estimating Change Intensity and Duration in Human Activity Recognition using Martingales

Jonathan Etumusei; Jorge Martinez Carracedo; Sally I McClean

doi:10.1007/978-3-030-95467-3_40

Estimating Change Intensity and Duration in Human Activity Recognition using Martingales

Jonathan Etumusei, Jorge Martinez Carracedo, Sally I McClean

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers
Subtitle of host publication	LOD 2021: Machine Learning, Optimization, and Data Science
Publisher	Springer
Pages	553-567
Number of pages	15
Volume	LNCS, volume 13163
ISBN (Electronic)	978-3-030-95467-3
ISBN (Print)	978-3-030-95466-6
DOIs	https://doi.org/10.1007/978-3-030-95467-3_40
Publication status	Published (in print/issue) - 2 Feb 2022
Event	The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science - Grasmere England, Lake Districk, United Kingdom Duration: 4 Oct 2021 → 8 Oct 2021 https://lod2021.icas.cc/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13163 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science
Abbreviated title	LOD2021
Country/Territory	United Kingdom
City	Lake Districk
Period	4/10/21 → 8/10/21
Internet address	https://lod2021.icas.cc/

Bibliographical note

Funding Information:
Supported by Ulster University.

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Change detection
martingales
human activity recognition
Human activity recognition
Martingales

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-95467-3_40

https://link.springer.com/chapter/10.1007/978-3-030-95467-3_40

Cite this

Etumusei, J., Martinez Carracedo, J., & McClean, S. I. (2022). Estimating Change Intensity and Duration in Human Activity Recognition using Martingales. In Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers: LOD 2021: Machine Learning, Optimization, and Data Science (Vol. LNCS, volume 13163, pp. 553-567). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13163 LNCS). Springer. https://doi.org/10.1007/978-3-030-95467-3_40

Etumusei, Jonathan ; Martinez Carracedo, Jorge ; McClean, Sally I. / Estimating Change Intensity and Duration in Human Activity Recognition using Martingales. Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers: LOD 2021: Machine Learning, Optimization, and Data Science. Vol. LNCS, volume 13163 Springer, 2022. pp. 553-567 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

keywords = "Change detection, martingales, human activity recognition, Human activity recognition, Martingales",

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Etumusei, J, Martinez Carracedo, J & McClean, SI 2022, Estimating Change Intensity and Duration in Human Activity Recognition using Martingales. in Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers: LOD 2021: Machine Learning, Optimization, and Data Science. vol. LNCS, volume 13163, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13163 LNCS, Springer, pp. 553-567, The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science , Lake Districk, United Kingdom, 4/10/21. https://doi.org/10.1007/978-3-030-95467-3_40

Estimating Change Intensity and Duration in Human Activity Recognition using Martingales. / Etumusei, Jonathan; Martinez Carracedo, Jorge ; McClean, Sally I.
Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers: LOD 2021: Machine Learning, Optimization, and Data Science. Vol. LNCS, volume 13163 Springer, 2022. p. 553-567 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13163 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Etumusei J, Martinez Carracedo J , McClean SI. Estimating Change Intensity and Duration in Human Activity Recognition using Martingales. In Machine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers: LOD 2021: Machine Learning, Optimization, and Data Science. Vol. LNCS, volume 13163. Springer. 2022. p. 553-567. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-95467-3_40

Estimating Change Intensity and Duration in Human Activity Recognition using Martingales

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this