Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest | [International Journal of Communication Networks and Information Security • 2022]

Author(s):

1. Neeraj Gupta: Department of Computer Science & Engineering, School of Engineering and Technology, K.R. Mangalam University, Gurugram, Haryana, India

2. S. Janani: Department of Electronics and Communication Engineering, Periyar Maniammai Institute of Science & Technology, Vallam, Tamil Nadu, India

3. Dilip R: Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology & Management, Udayapura, Bengaluru, India

4. Ravi Hosur: Department of Artificial intelligence, and Machine Learning, BLDEA's V. P. Dr. P. G. Halakatti College of Engineering & Technology, Vijayapur, Karnataka, India

5. Mr. Abhay Chaturvedi: Department of Electronics and Communication Engineering, GLA University, Mathura, Uttar Pradesh- 281406, India

6. Ankur Gupta: Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak- 124001, Haryana, India

Abstract:

In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.

Page(s): 179-188

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 2, Year: 2022

Keywords:

Sequential Minimization Optimization , Wearable sensors , 1D Local Binary Pattern , Gait Analysis

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