Recognition of Human Activity Using Machine Learning

Registration ID: IJNRD_192093

Published ID: IJNRD2304457

DOI: http://doi.one/10.1729/Journal.33898

Dr. S. Aruna , T. Prapulla Naidu , U. Rajasree , G. Satya , T. Divya Chandini,K. Ashisha Vijalani

The main purpose of this design is to achieve the certain exertion that is looking for by the system called Neural Networks which is like a heart in machine literacy. Device detectors give perceptivity into what persons are doing in real-time (walking, running, driving). Knowing the exertion of druggies allows, for case, to interact with them through an app. Artificial Neural Networks (ANNs) or Simulated Neural Networks are a subset of neural networks. Deep learning algorithms are built on the foundations of machine learning. Their name and structure are derived from the human brain, and they replicate the way organic neurons communicate with one another. The content of mortal exertion Recognition (HAR) is a prominent exploration area content in the field of computer vision and image processing area. It has empowered state- of- art operations in multiple sectors, including surveillance, digital entertainment, and medical healthcare. It is intriguing to observe and interesting to prognosticate similar kinds of movements. Several detector-grounded approaches have also been introduced to study and prognosticate mortal conditioning like accelerometer, gyroscope, etc., and it has their own advantages and disadvantages. In this paper, an intelligent mortal exertion recognition system is developed. Convolution Neural Networks (CNN) with spatiotemporal three-dimensional (3D) kernels are trained on a kinetics data set, which reflects people's actions in their daily lives and at work. As the notions of human activity recognition aid in understanding the concepts and issues of human action comprehension, which greatly aids in medication, management, learning patterns, and many circumstances involving video retrievals.

Published Paper   E-Certificate

Dr. S. Aruna, T. Prapulla Naidu, U. Rajasree, G. Satya, & T. Divya Chandini,K. Ashisha Vijalani (April-2023). Recognition of Human Activity Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e422-e427. http://doi.one/10.1729/Journal.33898

Volume 8 Issue 4, April-2023

Pages : e422-e427

Paper Reg. ID: IJNRD_192093

Published Paper Id: IJNRD2304457

Downloads: 000121987

Research Area: Electronics & Communication Engg. 

Country: Visakhapatnam, Andhra Pradesh, India

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Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304457

Crossref DOI: http://doi.one/10.1729/Journal.33898

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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