Human Activity Recognition Using Chaotic Logistic Map Guided Grey Wolf Optimization with Decision Tree
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 1 |
| Year of Publication : 2025 |
| Authors : M. Janaki, Sarojini Balakrishnan, S.N. Geethalakshmi |
10.14445/23488379/IJEEE-V12I1P113
How to Cite?
M. Janaki, Sarojini Balakrishnan, S.N. Geethalakshmi, "Human Activity Recognition Using Chaotic Logistic Map Guided Grey Wolf Optimization with Decision Tree," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 1, pp. 142-150, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I1P113
Abstract:
Wearable sensors are essential for recognizing human activity in sports, healthcare, and smart surroundings applications. Robust classification models and effective feature selection directly affect recognition accuracy. This paper proposes a novel approach called Chaotic Logistic Map-based Grey Wolf Optimization with Decision Tree (CLM-GWO-DT) to predict human activity recognition. The proposed technique improves the GWO algorithm by using chaotic logistic maps to enhance its exploration and exploitation abilities. CLM-GWO is used to find the most informative features in raw sensor data, thereby reducing dimensionality and enhancing relevant patterns. A Decision Tree (DT) classifier is then applied to the retrieved data to ensure accurate and interpretable identification of human activity. The experiments employed two popular datasets: UCI Human Activity Recognition (HAR) and Wireless Sensor Data Mining (WISDM). The results indicate that the proposed model exceeds the performance of existing methods in the literature concerning accuracy, precision, recall, F-Score, and Matthews Correlation Coefficient (MCC).
Keywords:
Chaotic logistic map, Feature selection, Grey wolf optimization, Human activity recognition.
References:
[1] Neha Gupta et al., “Human Activity Recognition in Artificial Intelligence Framework: A Narrative Review,” Artificial Intelligence Review, vol. 55, pp. 4755-4808, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ahatsham Hayat et al., “Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches,” Information, vol. 13, no. 6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Esther Omolara Abiodun et al., “A Systematic Review of Emerging Feature Selection Optimization Methods for Optimal Text Classification: The Present State and Prospective Opportunities,” Neural Computing and Applications, vol. 33, pp. 15091-15118, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Laith Abualigah et al., “Optimization Algorithms to Solve Feature Selection Problem: A Review,” International Journal of Science and Advanced Information Technology, vol. 8, no. 6, pp. 66-72, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Shitharth Selvarajan, “A Comprehensive Study on Modern Optimization Techniques for Engineering Applications,” Artificial Intelligence Review, vol. 57, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ahmed G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, pp. 2531-2561, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Quoc-Viet Pham, “Whale Optimization Algorithm with Applications to Resource Allocation in Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4285-4297, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Emmanuel Dada et al., “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons,” Gazi University Journal of Science, vol. 35, no. 2, pp. 485-504, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Samad Moslehi et al., “Application of Machine Learning Models Based on Decision Trees in Classifying the Factors Affecting Mortality of COVID-19 Patients in Hamadan,” BMC Medical Informatics and Decision Making, vol. 22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ibomoiye Domor Mienye, Yanxia Sun, and Zenghui Wang, “Prediction Performance of Improved Decision Tree-Based Algorithms: A Review,” Procedia Manufacturing, vol. 35, pp. 698-703, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Abbas Shah Syed et al., “Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mohamed E. Issa et al., “Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things,” Healthcare, vol. 10, no. 6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Daniel Garcia-Gonzalez et al., “A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors,” Sensors, vol. 20, no. 8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ahatsham Hayat et al., “Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches,” Information, vol. 13, no. 6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] K. ButchiRaju, “Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kun Xia et al., “LSTM-CNN Architecture for Human Activity Recognition,” IEEE Access, vol. 8, pp. 55000-55009, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Federico Cruciani et al., “Feature learning for Human Activity Recognition using Convolutional Neural Networks,” CCF Transactions on Pervasive Computing and Interaction, vol. 2, pp. 18-32, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Syed K. Bashar, Abdullah Al Fahim, and Ki H. Chon, “Smartphone-Based Human Activity Recognition with Feature Selection and Dense Neural Network,” 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, QC, Canada, pp. 5888-5891, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yihui Qiu, Xiaoxiao Yang, and Shuixuan Chen, “An Improved Grey Wolf Optimization Algorithm Solving Functional Optimization and Engineering Design Problems,” Scientific Reports, vol. 14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jhila Nasiri, and Farzin Modarres Khiyabani, “A Whale Optimization Algorithm (WOA) Approach for Clustering,” Cogent Mathematics and Statistics, vol. 5, no. 1, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Saman M. Almuft, Amar Yahya Zebari, and Herman Khalid Omer, “A Comparative Study of Particle Swarm Optimization and Genetic Algorithm,” Journal of Advanced Computer Science and Technology, vol. 8, no. 2, pp. 40-45, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] S. Mokhnache et al., “A Combined Chaotic System for Speech Encryption,” Engineering, Technology and Applied Science Research, vol. 12, no. 3, pp. 8578-8583, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Talha Umar, Mohammad Nadeem, and Faisal Anwer, “Chaos-Based Image Encryption Scheme to Secure Sensitive Multimedia Content in Cloud Storage,” Expert Systems with Applications, vol. 257, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Morsheda Akter et al., “Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination,” Sensors, vol. 23, no. 12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]