Final Year Project — Department of Computer Engineering, School of Engineering Sciences, University of Ghana, Legon.
- Overview
- The Problem
- Our Solution
- System Architecture
- Hardware Components
- Software & Technologies
- AI/ML Models
- Mobile App (Awopa)
- Results & Accuracy
- Project Structure
- Team
- Supervisors
- Acknowledgements
Pregnancy-related complications remain a major cause of maternal and infant mortality in developing countries. In Ghana, about 12% of female deaths (ages 15-49) over the past five years are attributed to pregnancy complications, with 62% of these deaths resulting from delayed or inadequate medical intervention.
This project delivers an IoT-based wearable device paired with a mobile application and AI predictive models to remotely monitor pregnant women's vital signs and predict complications like preeclampsia and anemia before they become life-threatening.
Wearable Device ──> Fog Computing (AI) ──> Cloud Server ──> Mobile App
(Sensors) (Predictions) (Storage) (Alerts & Dashboard)
WHY THIS MATTERS
┌─────────────────────────────────────────────────────────┐
│ 40% of pregnancies worldwide face health risks (WHO) │
│ 62% of maternal deaths in Ghana = delayed intervention │
│ 50%+ fatalities could be avoided with early detection │
└─────────────────────────────────────────────────────────┘
Pregnant women in Ghana face barriers to regular antenatal care including:
- Long travel distances to healthcare facilities
- High transportation costs and consultation fees
- Limited availability of diagnostic tools in rural hospitals
- Overburdened healthcare providers with high patient-to-doctor ratios
- No digital alert systems to warn of impending health risks
An integrated system with three core components working together:
A wrist-worn IoT device that continuously captures vital signs:
| Vital Sign | Sensor Used | Protocol |
|---|---|---|
| Heart Rate & SpO2 | MAX30102 | I2C |
| Blood Glucose (non-invasive) | AS7263 (NIR Spectroscopy) | I2C |
| Body Temperature | TMP117 | I2C |
| Motion / Activity | BMI270 (Accelerometer + Gyroscope) | I2C |
- Microcontroller: Heltec ESP32-S3
- Power: 3.7V Lithium Polymer (LiPo) battery
- Connectivity: Wi-Fi + Bluetooth Low Energy (BLE)
- Data Interval: Every 60 minutes, stored in EEPROM
- Casing: 3D-printed enclosure (designed in SolidWorks)
Machine learning models analyze vitals in real-time to predict:
- Preeclampsia — using blood pressure (systolic, diastolic, MAP) and protein in urine
- Anemia — using BMI, age, and risk factor indicators
- Flutter-based cross-platform mobile app called "Awopa"
- Role-based dashboards for Pregnant Women, Medical Officers, and Admins
- Cloud storage on AWS with MongoDB database
- AES-256 encryption for all health data
- Facial recognition authentication (Face-api.js)
┌──────────────┐ ┌──────────────────┐ ┌──────────────┐
│ PREGNANT │ │ WEARABLE │ │ CLOUD │
│ WOMAN │────>│ DEVICE │────>│ SERVER │
│ │ │ (ESP32-S3 + │ │ (AWS + MongoDB)
│ Registration│ │ 4 Sensors) │ │ │
└──────┬───────┘ └──────────────────┘ └──────┬───────┘
│ │
│ ┌──────────────────┐ │
│ │ AI PREDICTIVE │ │
└────────>│ MODELS │<───────────────┘
│ (Preeclampsia │
│ & Anemia) │
└────────┬─────────┘
│
┌────────v─────────┐
│ MOBILE APP │
│ (Awopa) │
│ │
│ - Vitals Display │
│ - Risk Alerts │
│ - Doctor Chat │
│ - Appointments │
│ - Chatbot │
└──────────────────┘
| Component | Purpose |
|---|---|
| Heltec ESP32-S3 | Main microcontroller with Wi-Fi & BLE |
| MAX30102 | Pulse oximetry — heart rate & blood oxygen (SpO2) |
| AS7263 | Near-infrared spectroscopy — non-invasive blood glucose |
| TMP117 | High-accuracy digital temperature sensor |
| BMI270 | 6-axis IMU — accelerometer + gyroscope for motion tracking |
| 3.7V LiPo Battery | Portable power supply |
| 3D-Printed Casing | Protective wrist-worn enclosure |
All sensors communicate via the I2C bus (SDA: GPIO 41, SCL: GPIO 42).
| Tool | Purpose |
|---|---|
| PlatformIO (VSCode) | Development environment |
| C++ | Firmware programming language |
| ESP-IDF / FreeRTOS | Real-time task scheduling |
| Tool | Purpose |
|---|---|
| Python 3.x | Primary language |
| Jupyter Notebook | Development environment |
| Scikit-learn | Model training & evaluation |
| Pandas / NumPy | Data processing |
| StandardScaler | Feature normalization |
| Tool | Purpose |
|---|---|
| Flutter | Cross-platform mobile framework |
| NestJS | Backend API framework |
| MongoDB | NoSQL database |
| Docker | Containerization |
| Redis | Caching |
| Apache Kafka | Event streaming |
| ElasticSearch | Search functionality |
| WebSockets | Real-time communication |
| Firebase Cloud Messaging | Push notifications |
| LangChain | Pregnancy chatbot AI |
| Face-api.js | Facial recognition auth |
| AWS S3 | Cloud storage |
The system uses Multiclass Logistic Regression classifiers to predict pregnancy complications with severity levels: No Risk, Mild, Moderate, and Severe.
| Metric | Score |
|---|---|
| Training Accuracy | 97% |
| Test Accuracy | 97% |
| Validation Accuracy | 97% |
Input Features: Systolic BP, Diastolic BP, Mean Arterial Pressure (MAP), Protein in Urine
| Metric | Score |
|---|---|
| Training Accuracy | 98% |
| Test Accuracy | 94% |
| Validation Accuracy | 93% |
Input Features: BMI Value, Age, Risk Factors (e.g., Excessive Vomiting)
Both models were validated against real clinical data from 30 pregnant women at Britannia Medical Centre (BMC) in Tema, Ghana, and reviewed by a medical consultant using blind test data.
The app provides three role-based dashboards:
- View real-time vitals (Blood Pressure, SpO2, Temperature, Heart Rate, Glucose)
- Create/cancel appointments
- Input protein in urine values
- Access pregnancy tips & info desk
- Interact with AI pregnancy chatbot
- Manage emergency contacts
- Receive risk alerts and notifications
- Write prescriptions
- Real-time chat with patients
- View live preeclampsia & anemia predictions
- Monitor patient glucose values
- Manage appointments
- Access preeclampsia risk assessments
- Manage notifications
- Handle support requests
- View all registered users
The system was tested with anonymized data from 30 pregnant women at Britannia Medical Centre (BMC), Tema, covering cases of anemia, preeclampsia, and normal conditions.
PREECLAMPSIA MODEL ANEMIA MODEL
┌─────────────────────┐ ┌─────────────────────┐
│ Train: 97% │ │ Train: 98% │
│ Test: 97% │ │ Test: 94% │
│ Valid: 97% │ │ Valid: 93% │
└─────────────────────┘ └─────────────────────┘
The wearable device measurements were also compared against standard medical reference devices (sphygmomanometer, pulse oximeter, thermometer) to verify sensor accuracy.
PregnancyMonitorProject/
├── DemonstrationVideo/ # Live testing video demo
│ └── LiveTesting.mp4
├── Materials/ # Research literature & references
│ ├── Others-Anemia/ # Anemia-related research papers
│ ├── ProblemStatement.pdf
│ └── ValidArticles/ # Thesis & extra research articles
│ ├── ThesisArticles/
│ └── ExtraArticles/
├── Poster/ # Project poster presentation
│ └── Wearable Poster.pptx
├── Presentations/ # All project presentations
│ ├── Defense/ # Final defense slides
│ ├── Progress/ # Progress report slides
│ ├── Proposal/ # Initial proposal slides
│ └── WeeklyMeetings/ # Weekly progress updates (WK1-Final)
├── Results/ # Test measurement data
│ └── WearableMeasurements.xlsx
├── Thesis/ # Final thesis document
│ ├── PregnancyMonitoringProject -GM.docx
│ └── PregnancyMonitoringProject -GM.pdf
├── PregnancyMonitoringProject_Thesis.pdf # Submitted thesis PDF
├── PregnancyMonitoringProject-Prof.Mills.docx
└── README.md # This file
| Name | Student ID |
|---|---|
| Owoh Einsteina Ofunne | 10943874 |
| Anane George Nyarko | 10947340 |
| Adika Nathaniel Agbesi Junior | 10957036 |
- Prof. Godfrey A. Mills — Supervisor
- Prof. Elsie Effah Kaufmann — Co-Supervisor
Special thanks to the collaborators who made this project possible:
- Dr. Kobby Appiah-Sakyi — Medical Consultant, Britannia Medical Centre
- Dr. Frank Amegah — Senior Resident, Korle-Bu Teaching Hospital
- Mr. Nathaniel Sakyi Adubier — Senior Research Engineer, IT Consortium
- IT Consortium — Project initiation and hardware funding
- Mr. Robert Sedohia — Chief Technician, Dept. of Computer Engineering
"Coming together is a beginning, staying together is progress, and working together is success."
University of Ghana, Legon | Department of Computer Engineering | October 2025