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 |
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Title of host publication | The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science |
Subtitle of host publication | LOD 2021: Machine Learning, Optimization, and Data Science |
Publisher | Springer |
Pages | 553-567 |
Number of pages | 14 |
Volume | LNCS, volume 13163 |
ISBN (Electronic) | 978-3-030-95467-3 |
ISBN (Print) | 978-3-030-95466-6 |
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/ |
Conference
Conference | The 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science |
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Abbreviated title | LOD2021 |
Country/Territory | United Kingdom |
City | Lake Districk |
Period | 4/10/21 → 8/10/21 |
Internet address |
Keywords
- Change detection
- martingales
- human activity recognition