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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

E. Dhiravidachelvi1, M.Suresh Kumar2, L. D. Vijay Anand3, D. Pritima4, Seifedine Kadry5, Byeong-Gwon Kang6, Yunyoung Nam7,*

1 Department of Electronics and Communication Engineering, Mohamed Sathak A.J. College of Engineering, Chennai, 603103, India
2 Department of Information Technology, Sri Sairam Engineering College, Chennai, 602109, India
3 Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
4 Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
5 Deparmtent of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Department of Information and Communication Engineering, Soonchunhyang University, Asan, Korea
7 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea

* Corresponding Author: Yunyoung Nam. Email: email

Computer Systems Science and Engineering 2023, 44(2), 961-977. https://doi.org/10.32604/csse.2023.024612

Abstract

Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques. These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment. Since wearable sensor-based HAR system includes in-built sensors, human activities can be categorized based on sensor values. Further, it can also be employed in other applications such as gait diagnosis, observation of children/adult’s cognitive nature, stroke-patient hospital direction, Epilepsy and Parkinson’s disease examination, etc. Recently-developed Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models can be deployed to accomplish effective outcomes on HAR process. With this motivation, the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR (IHPTDL-HAR) technique in healthcare environment. The proposed IHPTDL-HAR technique aims at recognizing the human actions in healthcare environment and helps the patients in managing their healthcare service. In addition, the presented model makes use of Hierarchical Clustering (HC)-based outlier detection technique to remove the outliers. IHPTDL-HAR technique incorporates DL-based Deep Belief Network (DBN) model to recognize the activities of users. Moreover, Harris Hawks Optimization (HHO) algorithm is used for hyperparameter tuning of DBN model. Finally, a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects. The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior performer compared to other recent techniques under different measures.

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APA Style
Dhiravidachelvi, E., Kumar, M., Anand, L.D.V., Pritima, D., Kadry, S. et al. (2023). Intelligent deep learning enabled human activity recognition for improved medical services. Computer Systems Science and Engineering, 44(2), 961-977. https://doi.org/10.32604/csse.2023.024612
Vancouver Style
Dhiravidachelvi E, Kumar M, Anand LDV, Pritima D, Kadry S, Kang B, et al. Intelligent deep learning enabled human activity recognition for improved medical services. Comput Syst Sci Eng. 2023;44(2):961-977 https://doi.org/10.32604/csse.2023.024612
IEEE Style
E. Dhiravidachelvi et al., "Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services," Comput. Syst. Sci. Eng., vol. 44, no. 2, pp. 961-977. 2023. https://doi.org/10.32604/csse.2023.024612



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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