Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA)
This study aims to investigate the alternative model structure based on a feature selection algorithm on multiple features-framework of human activity recognition (HAR) via wearable sensor-based modality. Neighborhood component analysis (NCA) is a linear transformation that maximizes the accuracy of...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10556512/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850219339019452416 |
|---|---|
| author | Norazman Shahar Muhammad Amir As'Ari Tan Tian Swee Nurul Fathia Ghazali |
| author_facet | Norazman Shahar Muhammad Amir As'Ari Tan Tian Swee Nurul Fathia Ghazali |
| author_sort | Norazman Shahar |
| collection | DOAJ |
| description | This study aims to investigate the alternative model structure based on a feature selection algorithm on multiple features-framework of human activity recognition (HAR) via wearable sensor-based modality. Neighborhood component analysis (NCA) is a linear transformation that maximizes the accuracy of specified classification events used as the benchmark for the proposed algorithm. Also, the effect of different combinations of sensor configurations of two, three, and all four sensors on the performance of the developed model was studied. The effectiveness and shortcomings of best sensor configuration were highlighted. Results were compared between different sensor configurations and benchmark HAR dataset. To maximize the regularization of NCA, fine-tuning the algorithm to maximize relevance and minimize redundancy (MRMR) was proposed. Results demonstrated that RNCA-MRMR could establish an efficient algorithm that can satisfy the model validation tests with significant advantages over feature number and predictive accuracy at 93.5%, 93.7%, and 94.5% for two, three, and all four sensors respectively. Furthermore, the adaptability of RNCA-MRMR to different data characteristics has ensured an optimal and task-specific representation of the data. In essence, the combined strength of RNCA and MRMR provides a versatile and effective approach for extracting meaningful features and enhancing the overall performance of machine learning models. |
| format | Article |
| id | doaj-art-2393a68cc45e449da03cb30ac2426c26 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2393a68cc45e449da03cb30ac2426c262025-08-20T02:07:24ZengIEEEIEEE Access2169-35362024-01-011211330011331310.1109/ACCESS.2024.341382210556512Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA)Norazman Shahar0https://orcid.org/0009-0001-2883-0036Muhammad Amir As'Ari1Tan Tian Swee2Nurul Fathia Ghazali3https://orcid.org/0000-0003-0679-7105Department of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaDepartment of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaDepartment of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaDepartment of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaThis study aims to investigate the alternative model structure based on a feature selection algorithm on multiple features-framework of human activity recognition (HAR) via wearable sensor-based modality. Neighborhood component analysis (NCA) is a linear transformation that maximizes the accuracy of specified classification events used as the benchmark for the proposed algorithm. Also, the effect of different combinations of sensor configurations of two, three, and all four sensors on the performance of the developed model was studied. The effectiveness and shortcomings of best sensor configuration were highlighted. Results were compared between different sensor configurations and benchmark HAR dataset. To maximize the regularization of NCA, fine-tuning the algorithm to maximize relevance and minimize redundancy (MRMR) was proposed. Results demonstrated that RNCA-MRMR could establish an efficient algorithm that can satisfy the model validation tests with significant advantages over feature number and predictive accuracy at 93.5%, 93.7%, and 94.5% for two, three, and all four sensors respectively. Furthermore, the adaptability of RNCA-MRMR to different data characteristics has ensured an optimal and task-specific representation of the data. In essence, the combined strength of RNCA and MRMR provides a versatile and effective approach for extracting meaningful features and enhancing the overall performance of machine learning models.https://ieeexplore.ieee.org/document/10556512/Accelerometeractivity recognitionfeature selectiongyroscopemachine learningwearable sensor |
| spellingShingle | Norazman Shahar Muhammad Amir As'Ari Tan Tian Swee Nurul Fathia Ghazali Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) IEEE Access Accelerometer activity recognition feature selection gyroscope machine learning wearable sensor |
| title | Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) |
| title_full | Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) |
| title_fullStr | Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) |
| title_full_unstemmed | Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) |
| title_short | Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) |
| title_sort | optimal activity recognition framework based on improvement of regularized neighborhood component analysis rnca |
| topic | Accelerometer activity recognition feature selection gyroscope machine learning wearable sensor |
| url | https://ieeexplore.ieee.org/document/10556512/ |
| work_keys_str_mv | AT norazmanshahar optimalactivityrecognitionframeworkbasedonimprovementofregularizedneighborhoodcomponentanalysisrnca AT muhammadamirasari optimalactivityrecognitionframeworkbasedonimprovementofregularizedneighborhoodcomponentanalysisrnca AT tantianswee optimalactivityrecognitionframeworkbasedonimprovementofregularizedneighborhoodcomponentanalysisrnca AT nurulfathiaghazali optimalactivityrecognitionframeworkbasedonimprovementofregularizedneighborhoodcomponentanalysisrnca |