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Model Optimization for Personalized Health Metrics Analysis
(Institute of Electrical and Electronics Engineers Inc., 2025) Perera, M; Wijesiriwardena, A; Pathirana, A; Gamaathige, L; Wijesiri, P; Jayakody, A
This paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. ensemble
Open Access
Review on vibration quality improvement of a passenger seat
(researchgate.net, 2019-03) Perera, M; Rikaz, M; Abeysinghe, A
Seats are one of the most significant components of vehicles. Customer’s expectations for the comfort in vehicle seats rise continuously. Designing of automobile seats has always been a challenge for engineers as design parameters for automobile seats are complex. When it comes to the design of automobile seats, three design objectives, namely comfort, safety and health need to be fulfilled simultaneously. Vibration analysis plays a major role in engineering including the area of design for automobile comfort. Human structure, as a mechanical system, is immensely complex and the mechanical properties of human body readily undergo change. In addition, the vibration can initiate the development of pressure ulcer and other long-term diseases. Comfort measurement is challenging because of such factors as user subjectivity, seat geometry, occupant anthropometry and amount of time spent sitting. The purpose of this study is to identify the most significant parameters that make vibration in the vehicle seat which reduce passenger comfort and to identify the design parameters that can impact on appropriate seat design. Suspension systems, seat cushion and sitting postures are some of the vital parameters that have huge impacts on comfort analysis. This review could help in design of seat cushions with appropriate material properties and in analysing the comfort levels of human body to reduce the vibration transmissibility at the critical frequency bands

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