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All Journal International Journal of Robotics and Control Systems
Keni, Nishad Nitin
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Control & Systems Engineering Electrical & Electronics Engineering

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IoT-AI in Healthcare: A Comprehensive Survey of Current Applications and Innovations Charfare, Ruwayd Hussain; Desai, Aditya Uttam; Keni, Nishad Nitin; Nambiar, Aditya Suresh; Cherian, Mimi Mariam
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1526

Abstract

The convergence of IoT and AI technology has the capacity to revolutionize healthcare by facilitating the gathering of real-time data and employing sophisticated analytics for tailored medical solutions. This survey provides an in-depth examination of IoT-AI applications in healthcare, specifically focusing on wearable devices such as smart bands and wristbands, as well as health monitoring systems. We present the core principles of IoT and AI, examining their synergistic integration in healthcare environments. The taxonomy of IoT-AI-based healthcare systems is comprehensive and classifies them according to their architectural components, data processing algorithms, and application domains. The survey showcases distinctive achievements, including novel methodologies for combining data and making predictions, frameworks for improving patient monitoring, and inventive methods for delivering healthcare remotely. We offer a comprehensive examination of key challenges such as data privacy, interoperability, and regulatory compliance, and analyze their specific effects on the implementation and efficacy of IoT-AI healthcare systems. The comparison analysis encompasses measures such as system performance, accuracy, and user satisfaction, providing valuable insights into the strengths and limitations of different techniques. In addition, we analyze developing patterns and clearly outline future areas of study, such as the enhancement of stronger security protocols, the use of blockchain technology to ensure data integrity, and the progress in AI algorithms to achieve more precise diagnoses. Emerging trends such as Digital Twins and SLUC are identified as promising avenues for future research. In conclusion, this study provides a detailed framework that enhances the understanding of IoT-AI healthcare systems and offers practical insights for improving healthcare practices and guiding technology adoption.
Smart Healthcare Framework: Real-Time Vital Monitoring and Personalized Diet and Fitness Recommendations Using IoT and Machine Learning Charfare, Ruwayd Hussain; Desai, Aditya Uttam; Keni, Nishad Nitin; Nambiar, Aditya Suresh; Cherian, Mimi Mariam
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1839

Abstract

Adopting a healthy lifestyle necessitates a well-balanced nutritional plan and personalized exercise routines aligned with an individual's health status. The healthcare system often lacks personalized care, leading to weak prevention and generic diets. This study presents an IoT-based framework for easy health monitoring without frequent doctor visits. The system integrates sensors to measure vital indicators like pulse rate, body temperature, SpO?, and BMI, with minimal assistance from healthcare personnel. Utilizing data gathered from individuals aged 16–25, ML algorithms like Logistic Regression, Random Forest, and KNN analyze the parameters to deliver personalized dietary and fitness recommendations. The dataset includes BMI, body temperature, pulse rate, and SpO2 measurements gathered via an integrated IoT unit. Before analysis, the data was refined and optimized through ML algorithms. This comprehensive approach moves beyond traditional diagnostic methods by incorporating personalized recommendations, including dietary plans and exercise routines, tailored based on the evaluated data. Among the evaluated algorithms, Random Forest demonstrated the highest accuracy (99%) in a 60:40 training-to-testing ratio. To improve accessibility, a user-friendly web platform is designed, facilitating seamless interaction and engagement. The framework unifies real-time monitoring, cardiovascular risk detection, and adaptive guidance, bridging fragmented digital health solutions for early intervention and better health outcomes.
Co-Authors Charfare, Ruwayd Hussain Cherian, Mimi Mariam Desai, Aditya Uttam Nambiar, Aditya Suresh