A Novel Martingale Model for Human Activity Recognition using Robust Optimisation Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The subject of human activity recognition is be- coming eminent in the study of human movement patterns and disabilities. Many existing approaches are effective in identi- fying change points in physical activities; however, algorithm optimisation could potentially be applied to improve some of these models. In this paper, we proposed a method known as mean of the geometric moving average of the Martingale sequence that can detect changes in human activity recognition. Furthermore, the suggested method is optimised for enhanced performance using meta-heuristic optimisation techniques based on the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithms. Experimentation shows that the suggested method improves the performance of the previous Martingale method.
Original languageEnglish
Title of host publication2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)979-8-3503-2297-2
ISBN (Print)979-8-3503-2298-9
DOIs
Publication statusPublished online - 22 Sept 2023
EventInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering - Hotel Escuela Santa Cruz , Santa Cruz de Tenerife, Spain
Duration: 19 Jul 202321 Jul 2023
Conference number: 2023
http://www.iceccme.com

Publication series

NameInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

Conference

ConferenceInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering
Abbreviated titleICECCME
Country/TerritorySpain
CitySanta Cruz de Tenerife
Period19/07/2321/07/23
Internet address

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Human activity recognition
  • Martingales
  • Optimisation
  • time series
  • tuning parameters

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