Robust Human Activity Recognition using Equilibrium Optimizer with Deep Extreme Learning Machine Model

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : L. Maria Anthony Kumar, S. Murugan, A. Therasa Alphonsa

10.14445/23488379/IJEEE-V10I5P101

How to Cite?

L. Maria Anthony Kumar, S. Murugan, A. Therasa Alphonsa, "Robust Human Activity Recognition using Equilibrium Optimizer with Deep Extreme Learning Machine Model," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 5, pp. 1-13, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P101

Abstract:

Recently, Human Activity Recognition (HAR) is becoming one of the prevalent study fields. HAR is a powerful tool for monitoring a person's dynamism, and it can be accomplished through machine learning (ML) techniques. HAR is a technique of automatically analysing and recognizing human activities depending on information from several wearable devices and smartphone sensors, like location, accelerometer, gyroscope, duration, and other environmental sensors. This study introduces a new Robust Human Activity Recognition using Equilibrium Optimizer with Deep Extreme Learning Machine (RHAR-EODELM) model. The presented RHAR-EODELM technique mainly identifies different classes of human activities. It follows a three-stage process. Initially, the RHAR-EODELM technique employs a min-max normalization process for scaling the activity data. Next, the RHAR-EODELM technique exploits a deep extreme learning machine with a radial basis function (DELM-RBF) model for the prediction process. Finally, the EO approach is enforced to adjust the parameters associated with the DELM-RBF method. A large-scale simulating process highlights the improved HAR results of the RHAR-EODELM method. The experimental values signify that the RHAR-EODELM method reaches improved predictive outcomes over other models.

Keywords:

Activity recognition, Brain-computer interface, Equilibrium optimizer, Machine learning, Parameter tuning.

References:

[1] Shibo Zhang et al., “Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances,” Sensors, vol. 20, no. 4, pp. 1-43, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Bolu Oluwalade et al., “Human Activity Recognition using Deep Learning Models on Smartphones and Smart Watches Sensor Data,” In Proceeedings of 14th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vittorio Mazzia et al.,  “Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition,” Pattern Recognition, vol. 124, pp.108487, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Fuqiang Gu et al., “A Survey on Deep Learning for Human Activity Recognition," ACM Computing Surveys (CSUR), vol. 54, no. 8, pp. 1-34, 2021.
[Google Scholar] [Publisher Link]
[5] K. Karthiga, and P. Karpagavalli, “An Efficient Human Tracking System using Local Binary Pattern and Cellular Non-Linear Networks," International Journal of P2P Network Trends and Technology, vol. 10, no. 5, pp. 1-6, 2020.
[CrossRef] [Publisher Link]
[6] Sakorn Mekruksavanich, and Anuchit Jitpattanakul, “Multimodal Wearable Sensing for Sport-Related Activity Recognition using Deep Learning Networks," Journal of Advances in Information Technology, vol, 13, no. 2, pp. 132-138, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wan Shaohua et al., “Deep Learning Models for Real-Time Human Activity Recognition with Smartphones," Mobile Networks and Applications, vol. 25, no. 2, pp. 743-755, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Rajit Nair et al., “Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition," Wireless Communications and Mobile Computing, vol. 2022, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] S. Aswath, and S. Valarmathi, “Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 134-144, 2022.
[Google Scholar] [Publisher Link]
[10] J. A. Smitha et al., “Optimized Routing on Wireless Body Sensor Network using Adaptive Lion Optimization Algorithm for IoT,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 189-197, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Pooja G Nair, and R. Sneha, “A Review: Facial Recognition using Machine Learning," International Journal of Recent Engineering Science, vol. 7, no. 3, pp. 85-89, 2020.
[CrossRef] [Publisher Link]
[12] Linkai Li, et al., “Integrated Access Control System of Face Recognition and Non-Contact Temperature Measurement Based on Arduino," International Journal of Computer and Organization Trends, vol. 12, no. 2, pp. 1-5, 2022.
[CrossRef] [Publisher Link]
[13] C. Nithyeswari, and G. Karthikeyan, “An Ensemble of Deep Learning with Optimization Model for Activity Recognition in the Internet of Things Environment,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 91-104, 2023.
[CrossRef] [Publisher Link]
[14] Yujie Wang et al., “Robust CSI-based Human Activity Recognition with Augmented Few-Shot Learning”, IEEE Sensors Journal, vol. 21, no. 21, pp. 24297-24308.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chaobo Li, Xulin Shen, and Hongjun Li, “S3GRN: Structural Similar Stepwise Generative Recognizable Network for Human Action Recognition with Limited Training Data”, IEEE Access, vol. 8, pp. 216219-216230, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Goutham Sakthivinayagam et al., “Violence Detection System using Convolution Neural Network,” SSRG International Journal of Electronics and Communication Engineering, vol. 6, no. 2, pp. 5-8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Santosh Kumar Yadav, Kamlesh Tiwari, Hari Mohan Pandey and Shaik Ali Akbar, “Skeleton-Based Human Activity Recognition using ConvLSTM and Guided Feature Learning”, Soft Computing, vol. 26, no. 2, pp. 877-890, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Muhammad Bilal et al., “A Transfer Learning-Based Efficient Spatiotemporal Human Action Recognition Framework for Long and Overlapping Action Classes," The Journal of Supercomputing, vol. 78, no. 2, pp. 2873-2908, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yong Li and Luping Wang, “Human Activity Recognition Based on Residual Network and BiLSTM," Sensors, vol. 22, no. 2, pp. 635, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Imran Ullah Khan, Sitara Afzal and Jong Weon Lee, “Human Activity Recognition via Hybrid Deep Learning Based Model," Sensors, vol. 22, no. 1, pp. 323, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Huaijun Wang, Jing Zhao, Junhuai Li, Ling Tian, Pengjia Tu, Ting Cao, Yang An, Kan Wang, and Shancang Li, “Wearable Sensor-Based Human Activity Recognition using Hybrid Deep Learning Techniques," Security and Communication Networks, vol. 2020, pp. 1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ohoud Nafea et al., “Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning," Sensors, vol. 21, no. 6, pp. 2141, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mohamed Abdel-Basset et al., “ST-DeepHAR: Deep Learning Model for Human Activity Recognition in IoT Applications," IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4969-4979, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Kamal A. ElDahshan, AbdAllah A. AlHabshy, and Bashar I. Hameed, “Meta-Heuristic Optimization Algorithm-Based Hierarchical Intrusion Detection System”, Computers, vol. 11, no. 12, pp. 170, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Dezheng Zhang, Peng Li, and Aziguli Wulamu, “An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm," Algorithms, vol. 15, no. 6, pp. 185, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Janandra Krishna Kishore Dokala et al., “A New Meta-Heuristic Optimization Algorithm Based MPPT Control Technique for PV System Under Diverse Partial Shading Conditions," 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] P. Dhivya Bharathy et al., “Hand Gesture Recognition for Physical Impairment Peoples,” SSRG International Journal of Computer Science and Engineering, vol. 4, no. 10, pp.6-10, 2017.
[CrossRef] [Publisher Link]
[28] [Online]. Available: https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
[29] [Online]. Available: http://sipi.usc.edu/had/
[30] Sakorn Mekruksavanich, and Anuchit Jitpattanakul, “Biometric User Identification based on Human Activity Recognition using Wearable Sensors: An Experiment using Deep Learning Models," Electronics, vol. 10, no. 3, pp. 308, 2021.
[CrossRef] [Google Scholar] [Publisher Link]