Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition
Human Activity Recognition (HAR) contributes significantly to vital areas in healthcare, IoT, and smart monitoring applications. Generally, the current models rely on deep learning and traditional machine learning techniques such as LSTM and SVM. However, these methods face considerable challenges,...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11036747/ |
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| author | Hayder K. Fatlawi Attila Kiss |
| author_facet | Hayder K. Fatlawi Attila Kiss |
| author_sort | Hayder K. Fatlawi |
| collection | DOAJ |
| description | Human Activity Recognition (HAR) contributes significantly to vital areas in healthcare, IoT, and smart monitoring applications. Generally, the current models rely on deep learning and traditional machine learning techniques such as LSTM and SVM. However, these methods face considerable challenges, such as imbalanced data distribution, weak adaptation of weights to temporal changes, and a decline in classification performance in rare activities. In this paper, we propose a new method based on dynamic reinforcement learning to update GOWLA weights, which is entitled Dynamic RL-GOWLA. In this method, the LSTM outputs are combined using Weighted Logarithmic Averaging with a reinforcement learning agent for dynamically updating the weights based on the model’s performance in each training iteration. Experiments on WISDM database show that the proposed method outperforms traditional models such as LSTM, SVM, Bayesian Classifier, RNN, and CNN, achieving a F1-score of 96.58%. This approach improves the adaptability of models to imbalanced data, making it a promising solution for tasks that require accurate classification of dynamic human activities. |
| format | Article |
| id | doaj-art-ab85ebaf36f4484db59be58ae9e9c8b9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ab85ebaf36f4484db59be58ae9e9c8b92025-08-20T02:21:25ZengIEEEIEEE Access2169-35362025-01-011310477910479010.1109/ACCESS.2025.357992611036747Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity RecognitionHayder K. Fatlawi0https://orcid.org/0000-0001-7243-8181Attila Kiss1https://orcid.org/0000-0001-8174-6194Center of Information Technology Research and Development, University of Kufa, Najaf, IraqDepartment of Information Systems, Eötvös Loránd University, Budapest, HungaryHuman Activity Recognition (HAR) contributes significantly to vital areas in healthcare, IoT, and smart monitoring applications. Generally, the current models rely on deep learning and traditional machine learning techniques such as LSTM and SVM. However, these methods face considerable challenges, such as imbalanced data distribution, weak adaptation of weights to temporal changes, and a decline in classification performance in rare activities. In this paper, we propose a new method based on dynamic reinforcement learning to update GOWLA weights, which is entitled Dynamic RL-GOWLA. In this method, the LSTM outputs are combined using Weighted Logarithmic Averaging with a reinforcement learning agent for dynamically updating the weights based on the model’s performance in each training iteration. Experiments on WISDM database show that the proposed method outperforms traditional models such as LSTM, SVM, Bayesian Classifier, RNN, and CNN, achieving a F1-score of 96.58%. This approach improves the adaptability of models to imbalanced data, making it a promising solution for tasks that require accurate classification of dynamic human activities.https://ieeexplore.ieee.org/document/11036747/Human activity recognitionreinforcement learningdeep learningordered weighted averaging |
| spellingShingle | Hayder K. Fatlawi Attila Kiss Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition IEEE Access Human activity recognition reinforcement learning deep learning ordered weighted averaging |
| title | Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition |
| title_full | Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition |
| title_fullStr | Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition |
| title_full_unstemmed | Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition |
| title_short | Dynamic Fusion of LSTM Predictions Using Reinforcement Learning-Based GOWLA for Human Activity Recognition |
| title_sort | dynamic fusion of lstm predictions using reinforcement learning based gowla for human activity recognition |
| topic | Human activity recognition reinforcement learning deep learning ordered weighted averaging |
| url | https://ieeexplore.ieee.org/document/11036747/ |
| work_keys_str_mv | AT hayderkfatlawi dynamicfusionoflstmpredictionsusingreinforcementlearningbasedgowlaforhumanactivityrecognition AT attilakiss dynamicfusionoflstmpredictionsusingreinforcementlearningbasedgowlaforhumanactivityrecognition |