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All Journal Emerging Science Journal
Anbananthen, Kalaiarasi Sonai Muthu
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Environmental Science

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Journal : Emerging Science Journal

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IoT-Driven Emotional Data Analytics for Medical Applications: Insights and Innovations Akila, D.; Pal, Souvik; Vijayarani, M.; Sarkar, Bikramjit; Anbananthen, Kalaiarasi Sonai Muthu; Muthaiyah, Saravanan
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-05

Abstract

This study introduces the Internet of Things-based Emotional State Detection Model (IoT-ESDM), a comprehensive and intelligent emotional computing framework aimed at detecting and managing anxiety-related behavior in healthcare environments. The model leverages a multi-modal approach that combines facial expression analysis, physiological signal monitoring, and AI-driven classification to accurately identify emotional states in real time. Core components of the system include fuzzy color filtering, histogram analysis, and virtual face modeling, which work together to extract relevant emotional features from input data. These features are then analyzed to provide adaptive, personalized feedback to patients or caregivers, enhancing emotional well-being support. Experimental results demonstrate the superior performance of IoT-ESDM over existing emotion detection systems. The model achieved a feedback ratio of 97.54%, accessibility ratio of 95.3%, detection accuracy of 92.7%, and a classification accuracy of 98.13%. Additionally, it showed a quality assurance rate of 94.13%, contributed to a 29.1% reduction in anxiety levels, and yielded a health outcome ratio of 94.5%. These metrics validate the system's effectiveness in clinical and real-world applications. The success of IoT-ESDM highlights its potential as a powerful tool for emotion-aware AI interventions, paving the way for future advancements in mental health monitoring and personalized healthcare solutions.
A Novel Approach on Covid-19 Contact Tracing – Utilization of Low Calibrated Transmission Power & Signal Captures in BLE Zaw, Thein Oak Kyaw; Muthaiyah, Saravanan; Anbananthen, Kalaiarasi Sonai Muthu; Soe, Min Thu
Emerging Science Journal Vol. 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-013

Abstract

Covid-19 pandemic has compelled countries to conduct contact tracing vigorously in order to curb the highly infectious virus from further spread. In this context, Bluetooth Low Energy (BLE) has been broadly used, utilizing Received Signal Strength Indicator (RSSI) for Close Contact Identification (CCI). However, many of the available solutions are not able to adhere to the guidelines provided by Centers for Disease Control (CDC) and Prevention which are: (1) Distance requirement of within 6-feet (~2 meters) and (2) Minimum 15-minutes duration for CCI. In providing some closure to the gap, we proposed a novel approach of utilizing: (1) Low calibrated transmission power (Tx) and (2) Number of signal captures. Our proposed approach is to lowly calibrate Tx so that when distance is at 2 meters between users, number signal capture gets lower as the chipset's smallest RSSI sensitivity value has been reached. In this paper, complete experimentation for Proof of Concept (POC) and Pilot test conducted are demonstrated. Results obtained shows that the accuracy for POC utilizing signal captures for 2 0.3 m distance is at: (1) 71.43% for 5 users and (2) 70.69% for 9 users. While so, accuracy for the Pilot test when considering CCI on individual case-basis is at 95% for 5 users. Doi: 10.28991/esj-2022-SPER-013 Full Text: PDF
The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study Zaw, Thein Oak Kyaw; Muthaiyah, Saravanan; Anbananthen, Kalaiarasi Sonai Muthu; Soe, Min Thu; Park, Byeonghwa; Kim, Myung Joon
Emerging Science Journal Vol. 6 (2022): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2022-SPER-019

Abstract

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions' elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PDF
Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19 Muthaiyah, Saravanan; Zaw, Thein Oak Kyaw; Anbananthen, Kalaiarasi Sonai Muthu; Park, Byeonghwa; Kim, Myung Joon
Emerging Science Journal Vol. 7 (2023): Special Issue "COVID-19: Emerging Research"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SPER-02

Abstract

The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PDF
Co-Authors Akila, D. Kim, Myung Joon Muthaiyah, Saravanan Park, Byeonghwa Sarkar, Bikramjit Soe, Min Thu Souvik Pal Vijayarani, M. Zaw, Thein Oak Kyaw