Advancing Health Monitoring With Cognitive IoT, Rapid Machine Learning, and Mechanical Systems-Reference-Cited by-同舟云学术

Advancing Health Monitoring With Cognitive IoT, Rapid Machine Learning, and Mechanical Systems

Published:2024-04-19 Issue: Volume: Page:295-306
ISSN:2328-8205
Container-title:Advances in Mechatronics and Mechanical Engineering
language:
Short-container-title:

Author:

Yeruva Ajay Reddy¹^ORCID,Jadhav Renuka Shankar²,Roopa R.³^ORCID,Preetha S.³,Priya R.⁴,Mishra Krishna Nand⁵,Karthick L.⁶^ORCID

Affiliation:

1. Independent Researcher, USA

2. Dr. D.Y. Patil Medical College Hospital and Research Center, Dr. D.Y. Patil Vidyapeeth, India

3. B.M.S. College of Engineering, India

4. Pollachi Institute of Engineering and Technology, India

5. Khwaja Moinuddin Chishti Language University, India

6. Department of Mechanical Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India

Abstract

Enhancing health monitoring for diabetes patients requires routine surveillance. Integrating IoT, embedded software, data analytics, intelligent systems, and smart devices can alleviate healthcare costs. Improved communication technologies enable remote exercise therapies. An intelligent healthcare infrastructure and expanded network packages are crucial for evolving e-health applications. Integration with 5G ensures higher bandwidth and energy efficiency. Real healthcare programs need seamless integration. In this study, an intelligent infrastructure for diabetes patient tracking using machine learning, smart gadgets, sensors, mobile phones, and mechanical systems ensure comprehensive data collection. Machine learning algorithms analyze patient data for efficient monitoring and prediction. Rigorous testing confirms system effectiveness.

Publisher

IGI Global

Reference14 articles.

1. Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia

2. Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

3. Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads

4. Cognitive Internet of things (CIoT) a success for data collection

5. García, Á. L., De Lucas, J.M., Antonacci, M., Zu Castell, W., David, M., Hardt, M., Lloret Iglesias, L., Moltó, G., Plociennik, M., … Tran, V. (2020). A cloud-based framework for machine learning workloads and applications. IEEE Access, 8, 18681–18692.