Building the Future Through
Research and Innovation

Overview

The Push Pull EV Charging System is an innovative electric vehicle charging solution designed to provide efficient, fast charging capabilities for EV users. The system addresses the limitations of conventional charging methods by offering a compact, user-friendly design that supports both AC and DC charging standards.

The project combines advanced power electronics, smart grid integration, and intuitive user interfaces to deliver a seamless charging experience while ensuring safety and reliability.

Problem Statement

Charging infrastructure for electric vehicles remains a significant barrier to their widespread adoption. Current charging solutions are often slow, inconvenient, and lack the flexibility needed for seamless integration into daily life.

This project aims to overcome these challenges by developing an innovative charging system that offers fast, efficient, and user-friendly charging capabilities, making electric vehicle ownership more accessible and convenient.

Achievement

• Achieved a Finalist position at the World Sustainable Development Goals Challenge 2026 held at Multimedia University Malaysia.
• Demonstrated the feasibility of an affordable, scalable system for future deployment in resource-constrained environments.

Overview

The Wearable Maternal Observatory Monitoring System is an IoT-enabled healthcare solution designed to provide continuous, real-time monitoring of maternal and fetal health throughout pregnancy. The system addresses the limitations of conventional hospital-based monitoring by offering a portable, wearable platform capable of tracking critical physiological parameters outside clinical settings. It is particularly intended for rural and underserved communities where access to specialized maternal healthcare is limited.

The project combines wearable sensing technologies, embedded systems, cloud computing, and web-based healthcare services to enable early detection of pregnancy-related complications and improve communication between expectant mothers and healthcare professionals.

Problem Statement

Pregnancy-related complications continue to be a leading cause of maternal mortality, especially in developing countries. Many existing monitoring techniques, such as Cardiotocography (CTG), require hospital visits, specialized equipment, and trained personnel. These systems are often expensive, immobile, and unsuitable for continuous monitoring.

This project aims to overcome these challenges by developing an affordable, wearable monitoring system capable of collecting and transmitting maternal and fetal health data in real time, enabling timely medical intervention and improving healthcare accessibility.

System Architecture

The proposed solution consists of four integrated layers:

• Wearable Sensing Layer: Collects maternal and fetal physiological signals using IoT sensors.
• Embedded Processing Layer: ESP32 microcontroller processes sensor data and transmits it wirelessly via Wi-Fi/Bluetooth.
• Cloud & Database Layer: Stores patient records securely using a MySQL database with PHP-based backend services.
• User Interface Layer: Web dashboards provide real-time monitoring, historical data visualization, digital prescriptions, and secure access for both mothers and healthcare providers.

Key Features

• Continuous real-time maternal and fetal health monitoring
• Wearable and non-invasive sensing system
• Monitoring of maternal Heart Rate, ECG, SpO₂, body temperature, and physical activity
• Fetal ECG and oxygen saturation monitoring
• Secure cloud-based data storage and synchronization
• Separate dashboards for patients and healthcare professionals
• Historical health data visualization and trend analysis
• Digital prescription management
• Real-time emergency alerts for abnormal health conditions
• AI-powered chatbot for basic healthcare guidance and consultation
• Remote monitoring through web and mobile platforms

AI Feature

The system incorporates an AI-based anomaly detection model that continuously analyzes maternal and fetal vital signs to identify abnormal physiological patterns. Instead of relying solely on threshold-based alerts, the AI predicts potential health risks by recognizing deviations from normal trends, enabling earlier intervention and reducing the likelihood of severe pregnancy complications.

Future enhancements include personalized risk prediction models, predictive maternal health analytics, and machine learning-based pregnancy outcome assessment.

Technologies Used

• ESP32 Microcontroller
• Embedded C
• IoT Health Sensors
• Wi-Fi & Bluetooth Communication
• PHP Backend with MySQL Database
• HTML, CSS, JavaScript
• Cloud-based Data Management
• Wearable Electronics

Methodology

1. Selected biomedical sensors based on maternal and fetal health parameters.
2. Developed an ESP32-based wearable prototype for real-time data acquisition.
3. Implemented wireless communication for cloud synchronization.
4. Designed secure backend services with role-based authentication.
5. Developed interactive dashboards for mothers and healthcare providers.
6. Integrated chatbot functionality and emergency alert mechanisms.
7. Conducted controlled prototype testing to validate data acquisition, visualization, and remote monitoring performance.

Results

Prototype evaluation demonstrated successful real-time monitoring and transmission of maternal and fetal health data. Key physiological parameters—including heart rate, oxygen saturation, body temperature, and fetal ECG—were accurately measured and visualized through the web dashboard.

Healthcare providers were able to review historical records, monitor patient status remotely, and issue digital prescriptions. The system showed strong potential for scalable deployment, although further work is required on sensor calibration, signal noise reduction, large-scale clinical validation, and operation in areas with limited internet connectivity.

Challenges

• Sensor calibration and biomedical signal accuracy
• Motion artifacts and signal noise
• Limited internet connectivity in rural regions
• Wearable device acceptance among target populations
• Digital literacy barriers
• Clinical validation with larger patient groups

Applications

• Maternal and fetal healthcare monitoring
• Smart hospitals and maternity clinics
• Remote patient monitoring
• Community healthcare programs
• Rural and low-resource healthcare systems
• High-risk pregnancy monitoring

Achievement

• Achieved a Top 10 Finalist position at the IEEE YESIST12 International Innovation Challenge held at Universiti Kebangsaan Malaysia.
• Demonstrated the feasibility of an affordable, scalable, and wearable maternal healthcare platform suitable for future deployment in resource-constrained environments.

Overview

The Cold Chain Logistic Systems is an innovative logistic system for cold chain. The system addresses the limitations of freezing system.