Estimating Change Intensity and Duration in Human Activity Recognition using Martingales

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers
Subtitle of host publicationLOD 2021: Machine Learning, Optimization, and Data Science
PublisherSpringer
Pages553-567
Number of pages15
VolumeLNCS, volume 13163
ISBN (Electronic)978-3-030-95467-3
ISBN (Print)978-3-030-95466-6
DOIs
Publication statusPublished (in print/issue) - 2 Feb 2022
EventThe 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science - Grasmere England, Lake Districk, United Kingdom
Duration: 4 Oct 20218 Oct 2021
https://lod2021.icas.cc/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science
Abbreviated titleLOD2021
Country/TerritoryUnited Kingdom
CityLake Districk
Period4/10/218/10/21
Internet address

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

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