Alzheimer’s disease (AD) is a progressive neurological disorder causing memory loss, cognitive decline, and loss of independence. It is a leading cause of disability among the elderly, affecting millions worldwide. By 2030, an estimated 74.7 million people will be diagnosed with AD, increasing to 107 million by 2050.
A major challenge faced by AD patients is wandering and disorientation, leading to safety concerns:
- 41% of patients get lost outside their homes.
- 30% get lost inside their own homes (UK data).
- 70.7% of AD patients in Taiwan report wandering incidents.
Additionally, AD patients have an increased risk of falls, leading to serious injuries due to postural imbalances and motor impairments.
Therefore, this project presents a Smart Wearable Medical Device (SWMD)—an advanced IoT-based hand-band designed to:
- Track patients' location via GPS.
- Detect falls and notify caregivers immediately.
- Send emergency alerts in critical situations.
- Monitor vital biomarkers, including temperature, heart rate, and body movement.
It consists of:
- A wearable device embedded with multiple sensors for real-time health monitoring.
- A cloud-connected application using Firebase to store, synchronize, and display data for caregivers.
| Feature | Description |
|---|---|
| Real-Time Monitoring | Continuously tracks heart rate, blood pressure, and oxygen saturation for early detection of health issues. |
| GPS Tracking | Enables caregivers to instantly locate patients, reducing the risk of wandering and ensuring safety. |
| User-Friendly Mobile Application | Displays real-time health data and sends alerts for abnormal readings or emergency situations. |
| Firebase Cloud Storage | Uses Google Firebase for secure, real-time data storage and synchronization. |
| Communication Protocols | Utilizes Bluetooth Low Energy (BLE) and Wi-Fi for seamless and power-efficient data transmission. |
| Regulatory Compliance | Designed to meet FDA and CE medical device regulations, ensuring safety and reliability. |
- MPU-6050 Acc & Gyro Sensor
- MAX30100 Pulse Oximeter and Heart Rate Sensor
- Texas Instrument LM35 Temperature Sensor
- GPS GPS6MV2 module (Ultimate GPS FeatherWing)
- LCD Display (1.8 inches)
- Buzzer
- 3.7V Lithium-Ion Battery (2000 mAh)
- ESP32-WROOM-32 Particle Boron Microcontroller Module
- SIM800L Module
The system consists of multiple input sensors, a microcontroller, batteries, and output components to provide real-time health monitoring and fall detection for Alzheimer's patients. It provides two primary outputs:
- Sounds an alert when abnormal health readings or falls are detected.
- Sends real-time sensor data to a cloud-hosted database (Firebase) for remote monitoring.
- The system turns on and connects to the GSM module. If unsuccessful, the device powers off.
- Sensors capture and analyze health parameters and movement every 10 seconds.
- The microcontroller processes and uploads data to Firebase, ensuring real-time access.
- If fall detection or abnormal readings occur, the buzzer sounds, and an alert is sent via the cloud to caregivers.
Table 1: Connections between the Particle Boron and the Ultimate GPS FeatherWing
| Particle Boron | Ultimate GPS FeatherWing |
|---|---|
| 3.3V | 3.3V |
| GND | GND |
| TX(D9) | RX |
| RX(D10) | TX |
Table 2: Connections between the Particle Boron and MLX90614 Temperature Sensor
| Particle Boron | MLX90614 Temperature Sensor |
|---|---|
| 3.3V | Vin |
| GND | GND |
| SCL(D1) | SCL |
| SDA(D0) | SDA |
Table 3: Connections between the Particle Boron and MPU6050 Acc & Gyro Sensor
| Particle Boron | MPU6050 Acc & Gyro Sensor |
|---|---|
| 3.3V | Vin |
| GND | GND |
| SCL(D1) | SCL |
| SDA(D0) | SDA |
Table 4: Connections between the Particle Boron and the MAX 30100 Heart Oximeter sensor
| Particle Boron | MAX 30100 Heart Oximeter |
|---|---|
| 3.3V | Vin |
| GND | GND |
| SCL(D1) | SCL |
| SDA(D0) | SDA |
| Home Page | Readings Page |
|---|---|
 |
 |
Designed and Packaged as a Certified Medical Device:
- The prototype is fully labeled and packaged for medical use.
- A detailed User Manual is available: User Manual.
A full analysis of risks, which includes details on risk identification, risk evaluation (matrix analysis), and risk control measures, is available: Risk Analysis.
- Class IIb: The system is classified as Class IIb under CE medical regulations. Falls under Rule 10 "Devices monitoring vital physiological processes where variations can pose immediate danger".
- Class I: Based on similar fall detection devices, the system meets FDA Class I criteria.
This project is licensed under the MIT License.
You are free to use, modify, and distribute this project for educational and research purposes, but proper credit must be given to the original author (Noora-Alhajeri).
© 2024 Noora-Alhajeri. All rights reserved.
Originally Developed: June 15, 2022 Uploaded to GitHub: 2024
For questions or collaboration, feel free to reach out:
📧 Email: n.s3eedalhajeri@gmail.com
🌐 LinkedIn: Noora-Alhajeri