The report presents HealthPal, a free, web-based health and wellness platform designed to help individuals monitor, manage, and improve their health through AI-driven insights and personalized recommendations. Unlike many commercial apps, HealthPal emphasizes accessibility and inclusivity, aiming to empower users—regardless of financial status—to take proactive control of their well-being. It supports tracking of BMI, sleep, hydration, activity, and workouts, and offers tailored yoga and exercise plans.
The literature review explores existing health monitoring systems, highlighting methodologies like machine learning (ML), Bayesian networks, simulations, and prototype development. While current research showcases technological potential, common issues include lack of standardization, connectivity dependence, limited personalization, and accessibility challenges. HealthPal seeks to address these gaps.
The methodology for developing HealthPal involved user needs analysis, front-end and back-end web development, secure login, health data entry, visualization tools, and AI-based health suggestions. It includes features like progress tracking, streak counters, goal-setting, and downloadable reports, with plans for wearable integration in the future.
Key research gaps addressed include:
Lack of free and inclusive platforms
Insufficient personalization
Fragmented health tools
Weak goal-tracking mechanisms
Limited real-world application of academic models
HealthPal stands as a practical implementation of academic concepts in health informatics, offering a user-friendly, all-in-one wellness platform that bridges theory and real-world use.
Conclusion
The project aims to develop a user-friendly health management platform that allows users to log and monitor their daily health activities such as water intake, sleep duration, steps walked, and workout time. The system will enable users to track their progress dynamically through visual dashboards and receive smart suggestions to improve their wellness.
A secure login and registration system is implemented to ensure personalized user experience. Once logged in, users can easily record their health metrics, set personal goals, and monitor goal completion through progress bars, streak counters, and detailed analytics.
References
[1] Rossi, L., Bianchi, A., & Marino, F. (2024). Cloud-Integrated Wearable Devices for Emergency Health Alerts: Design and Implementation. University of Piemonte OrientaleVercelli, Italy
[2] Greco, S., Romano, P., & Valli, D. (2024). Adaptive Sensor Systems for Remote Health Monitoring in Elderly Care. University of Piemonte Orientale, Vercelli, Italy
[3] Griffin, A. (2018). mHealth Technologies for Health Data Capture. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[4] Soundarya, S. (2019). Smart Health Monitoring System using IoT. International Journal of Engineering Research & Technology (IJERT)
[5] Hassan, M., Lata, T. R., Rani, N., Al-Amin, M., Shak, M. A., Mia, L., Islam, M. Z., & Bagdadee, A. H. (2023). IoT-Based Smart Health Monitoring System for Efficient Service in the Medical Sector.
[6] Mishra, S., Dubey, R. R. K., Mahapatra, S., Rajak, U. K., Balakarthikeyan, M., & Shukla, K. K. (2025). Design and Implementation of Electronics based IoT-Enabled Smart Health Monitoring System.
[7] Kuo, Y.-W., Tsao, Y.-C., Chien, W.-C., Huang, Y.-M., & Liao, L.-D. (2022). Smart Health Monitoring and Management System for Organizations Using RFID Technology in Hospitals or Emergency Applications.
[8] S., Kaikade, V., & Panse, S. (2021). Internet of Things (IoT) Based Smart Health Monitoring System – A Case Study. International Journal of Computational and Electronic Aspects in Engineering.
[9] Xiaoyan Li- (2022) - Tracking and Monitoring System in Designed Boundary for Cardiology Patients
[10] Javier Cabrera- New Health monitoring system-(2021) - Real-time data analysis in health monitoring systems: A comprehensive systematic literature review
[11] Kangwon You- 2022- Personalized Telehealth Systems. - International Journal of Computational and Electronic Aspects in Engineering.
[12] Alzaleq, D. (2021). Analyzing Health Data for Trends. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[13] Sheng, N. (2024). IoT Applications in Patient Monitoring. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[14] Ianculescu, M. (2024). AI tools for healthcare management. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[15] Coman, L. (2024). The role of machine learning in healthcare. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[16] Wang, H. (2020). Personalized Telehealth Systems. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[17] Chung, E. (2018). Digital Approaches to Patient Health Monitoring. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[18] Griffin, A. (2018). mHealth Technologies for Health Data Capture. University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA.
[19] Du\'a Alzaleq-(2021)- Health tracker: data acquisition and analysis- A Personalized Health Monitoring System for Community-Dwelling Elderly People
[20] Nan Sheng-(2024)- Neural Network health monitoring system- Health tracker: data acquisition and analysis for monitoring health trends and assessing disease risk.