Version 1
: Received: 20 June 2024 / Approved: 21 June 2024 / Online: 23 June 2024 (04:20:43 CEST)
How to cite:
Vazan, M.; Sharifi, E.; Farahani, H.; Madadi, S. Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy. Preprints2024, 2024061528. https://doi.org/10.20944/preprints202406.1528.v1
Vazan, M.; Sharifi, E.; Farahani, H.; Madadi, S. Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy. Preprints 2024, 2024061528. https://doi.org/10.20944/preprints202406.1528.v1
Vazan, M.; Sharifi, E.; Farahani, H.; Madadi, S. Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy. Preprints2024, 2024061528. https://doi.org/10.20944/preprints202406.1528.v1
APA Style
Vazan, M., Sharifi, E., Farahani, H., & Madadi, S. (2024). Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy. Preprints. https://doi.org/10.20944/preprints202406.1528.v1
Chicago/Turabian Style
Vazan, M., Hadi Farahani and Sadegh Madadi. 2024 "Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy" Preprints. https://doi.org/10.20944/preprints202406.1528.v1
Abstract
This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.
Keywords
Feature Extraction; Human Activity Recognition; Linear Discriminant Analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
