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 language | English |
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Title of host publication | 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-2297-2 |
ISBN (Print) | 979-8-3503-2298-9 |
DOIs | |
Publication status | Published online - 22 Sept 2023 |
Event | International Conference on Electrical, Computer, Communications and Mechatronics Engineering - Hotel Escuela Santa Cruz , Santa Cruz de Tenerife, Spain Duration: 19 Jul 2023 → 21 Jul 2023 Conference number: 2023 http://www.iceccme.com |
Publication series
Name | International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 |
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Conference
Conference | International Conference on Electrical, Computer, Communications and Mechatronics Engineering |
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Abbreviated title | ICECCME |
Country/Territory | Spain |
City | Santa Cruz de Tenerife |
Period | 19/07/23 → 21/07/23 |
Internet address |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Human activity recognition
- Martingales
- Optimisation
- time series
- tuning parameters