Robust Approach for Human Activity Recognition Using Decomposing Technique Based Machine Learning Models
Paper
Paper/Presentation Title | Robust Approach for Human Activity Recognition Using Decomposing Technique Based Machine Learning Models |
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Presentation Type | Paper |
Authors | Abdulla, Suha Zadain, Diykh, Mohammed, Abdulla, Shahab, Alabdally, Hussein and Sahi, Aqeel |
Journal or Proceedings Title | Proceedings of the13th International Conference on Health Information Science (HIS 2025) |
Journal Citation | 15336, pp. 281-291 |
Number of Pages | 11 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Singapore |
ISBN | 9789819655960 |
9789819655977 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-96-5597-7_25 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-96-5597-7_25 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-96-5597-7 |
Conference/Event | 13th International Conference on Health Information Science (HIS 2024) |
Event Details | 13th International Conference on Health Information Science (HIS 2024) Parent International Conference on Health Information Science (HIS) Delivery Online Event Date 08 to end of 10 Dec 2024 Event Location Hong Kong, China |
Abstract | Analysis of human behaviour in IoT applications based on human interaction is the area in which human activity recognition has drawn a lot of attention. In this paper, we proposed an intelligent model integrating multivariate dynamic mode decomposition (MDMD) and ensemble machine learning model to recognise physical human activity. The sensor data is decomposed into dynamic modes using MDMD. Different features including statistical features, power, average absolute amplitude, and frequency are derived from each mode to represent different classes of human activity. To classify the exacted features, several ensemble learning models are employed. The proposed model’s performance is evaluated using two datasets, UCI-HAR, and WISDM. The proposed model obtained remarkable accuracies of 97.6, and 95.5%, F1-score of 95%, and 93.20% for UCI-HAR, and WISDM respectively. Our findings proved that the proposed model is superior to competing previous models. |
Keywords | features extraction; HAR; MDMD; ensemble model |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460201. Artificial life and complex adaptive systems |
Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | UniSQ College |
University of Thi-Qar, Iraq | |
Al-Ayen University, Iraq |
https://research.usq.edu.au/item/zx7v3/robust-approach-for-human-activity-recognition-using-decomposing-technique-based-machine-learning-models
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