• Register
  • Login

European Journal of Molecular & Clinical Medicine

  1. Home
  2. Machine Learning Trained Edge Computing Device for PhysicallyDisabled

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Machine Learning Trained Edge Computing Device for PhysicallyDisabled

    Author

    • U.Vijaya Laxmi, V.Vijaya Ramaraju , P.Srividya Devi
,

Document Type : Research Article

  • Article Information
  • Download
  • Export Citation
  • Statistics
  • Share

Abstract

Biomedical devices play a crucial role in community as these are revolutionizing with breath taking approach in both the medication and the exposure of many diseases. The paper aims to Design edge-based home automation using ESP-32 for Physically Disabled People. Edge computing is an applicable manner to meet the immense estimation and flat-dormancy conditions of deep learning on edge devices and implements increased interests in isolation, bandwidth efficiency, and expandability. ESP-32 receives data from the sound sensor and recognizes the voice command which is already trained and trigger the relay. Automated system using ESP-32 with voice command controls the home appliances. The paper mainly focuses on disabled people to facilitate integrated system that is easy–to–use using Machine learning Technique. The home automation system allows one to control household appliance centralize wireless control unit. This paper aims to control home appliances with user handy, economical, effort less installation for Physically disabled people

Keywords

  • Deep learning
  • ESP-32
  • Home Automation
  • Supervised Machine Learning
  • XML
  • PDF 885.42 K
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
    • Article View: 15
    • PDF Download: 93
European Journal of Molecular & Clinical Medicine
Volume 9, Issue 7
September 2022
Page 8994-9001
Files
  • XML
  • PDF 885.42 K
Share
Export Citation
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
Statistics
  • Article View: 15
  • PDF Download: 93

APA

P.Srividya Devi, U. L. V. R. ,. (2023). Machine Learning Trained Edge Computing Device for PhysicallyDisabled. European Journal of Molecular & Clinical Medicine, 9(7), 8994-9001.

MLA

U.Vijaya Laxmi, V.Vijaya Ramaraju , P.Srividya Devi. "Machine Learning Trained Edge Computing Device for PhysicallyDisabled". European Journal of Molecular & Clinical Medicine, 9, 7, 2023, 8994-9001.

HARVARD

P.Srividya Devi, U. L. V. R. ,. (2023). 'Machine Learning Trained Edge Computing Device for PhysicallyDisabled', European Journal of Molecular & Clinical Medicine, 9(7), pp. 8994-9001.

VANCOUVER

P.Srividya Devi, U. L. V. R. ,. Machine Learning Trained Edge Computing Device for PhysicallyDisabled. European Journal of Molecular & Clinical Medicine, 2023; 9(7): 8994-9001.

  • Home
  • About Journal
  • Editorial Board
  • Submit Manuscript
  • Contact Us
  • Glossary
  • Sitemap

News

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

Newsletter Subscription

Subscribe to the journal newsletter and receive the latest news and updates

© Journal Management System. Powered by ejournalplus