Machine Learning Based Efficient Human Activity Recognition System

Authors

  • V. Seedha Devi Associate Professor, Department of IT, Jaya Engineering College, Chennai 602024, India
  • K. Sumathi Associate Professor, Department of ECE, Sri Sairam Engineering College, Chennai 600044, India
  • M. Mahalakshmi Assistant Professor, Department of Networking and Communications, SRM Institute of Science and Technology, Chennai 603203, India
  • A. Jose Anand Professor, Department of ECE, KCG College of Technology, Chennai 600097, India
  • Anita Titus Professor, Department of ECE, Jeppiaar University, Chennai - 600113, India
  • N. Naga Saranya Associate Professor, Department of MCA, Meenakshi College of Engineering, Chennai - 600078, India

Keywords:

Machine Learning, IoT Sensors, Human Activity Reorganization, SVM, KNN

Abstract

Human actions pose a serious issue in many different fields. The intriguing potential in this area includes smart homes, assistive robotics, human-computer interfaces, and security upgrades, to name just a few. The cornerstone for the creation of potential applications in the areas of health, wellness, and sports is in particular activity recognition. Applications for Human Activity Recognition (HAR) are numerous due to its effect on wellbeing. The display of people presents a substantial challenge for the analysis of human behaviour through activities. The success of machine learning (ML) techniques in many applications stimulates their use in data analysis as they become more sophisticated. The routine collection and saving of data from Internet of Things (IoT) sensors, which is used to support decision-making, has also been made simpler by recent developments in advanced technology. Conversely, there is a crucial requirement to collect and organize patient data in electronic arrangement in the mainstream of the countries. The composed data will then be scrutinized for a diagnosis, a prediction, and probable therapies dependent on the patient's admissibility. The Wireless Sensor Data Mining (WISDM) Smartphone and Smart watch Activity and Biometrics Dataset is used in this study to forecast human activity. In this work, numerous human actions were used to train machine learning models.  K-Nearest Neighbour (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) methods are used to analyse with the novel model named, features-based fused SVM-KNN approach. The suggested model is superior to the other algorithms, according to the results.

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Published

24.11.2023

How to Cite

Devi, V. S. ., Sumathi, K. ., Mahalakshmi, M. ., Anand, A. J. ., Titus, A. ., & Saranya, N. N. . (2023). Machine Learning Based Efficient Human Activity Recognition System. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 338–346. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3895

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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