DEVELOPMENT OF THE INTERNET OF ROBOTIC THINGS FOR SMART AND SUSTAINABLE HEALTH CARE

Gabriel Gregory James; Michael Nseobong Archibong; Onuodu F. E.; Etimbuk Emmanuel Abraham; Peace C. Okafor; Ufford Victor Ufford

doi:10.29121/shodhai.v1.i1.2024.3

Authors

Gabriel Gregory James Lecturer, Department of Mathematics and Computer Sciences, Ritman University, Mkpatak, Nigeria
Michael Nseobong Archibong Lecturer, Department of Computing, Topfaith University, Mkpatak, Nigeria
Onuodu, F. E. Lecturer, Department of Computer Science, University of Port Harcourt, Nigeria
Etimbuk Emmanuel Abraham Lecturer, Department of Electrical Electronics Engineering, Topfaith University Mkpatak, Nigeria
Peace C. Okafor Officer, Department of State Service, Bayelsa State Command, Bayelsa, Nigeria
Ufford Victor Ufford M.Sc. (Ed.) Candidate, Department of Computer and Robotics Education, University of Uyo, Uyo, Nigeria

DOI:

https://doi.org/10.29121/shodhai.v1.i1.2024.3

Keywords:

Data Analytics, Health Issues, IoRT, Robotics

Abstract

There has been an alarming increase in sick patients in rural and remote areas. This is a result of non-operational healthcare infrastructure and the unavailability of doctors. Several elderlies in Nigeria are plagued with major health issues such as hearing loss, neck pain, and cataracts. This is due to the lack of a robust information system for visualized activity guidance for sick patients in rural areas. To solve this problem, there is a need for an enhanced Internet of Robotic Things model for data analysis and interpretation of health-related issues which is highly indispensable. The study presented an optimized approach to the application of the Internet of Robotic Things and data analytics in the health sector. The Internet of Robotic Things (IoRT) is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. In this work, an enhanced Internet of Robotic Things (IoRT) model for the health activity of patients in rural areas was developed using Object-oriented system design methodology (OOSDM). The new system was optimized with neuro-fuzzy technique and implemented with JAVA, XAMPP, Hypertext Pre-processor (PHP) programming language, Bootstrap, JavaScript, Cascading Style Sheet (CSS), and MySQL for medical-related datasets storage. The results obtained showed that parameters for both systems were used to evaluate their performance in the number of analyzed data, the number of identified health issues, the number of robots integrated into the system, the number of authentication techniques for registered users of the system and the number of adopted machine-learning technique. The new system surpassed the existing in all the evaluated parameters and adopted a total number of 100 records which enabled it to identify 3 health issues. The identified and addressed issues were loss of hearing, cataracts, and neck pain. The new system also integrated only a robot for patient interaction. The new system utilized the username and password technique and the iris-based technique for user authentication. In addition, the new system could be beneficial to sick patients with life-threatening issues, to medical experts, and researchers with a keen interest in the study area.

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