CityU EE : FYP - Health Monitoring
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Introduction
Introduction
I am Ching Hong DMs and I am Choi Ji, a Master of Electronic Engineering student at the City University of Hong Kong. My project focuses on Mobile Device and Cloud Server-Based Intelligence Health Monitoring Systems. The primary objective of this project is to develop a wireless motion sensor that automatically monitors and alerts for signs of falls in elderly individuals.
Functionality of the Device
When the project is completed, the device will offer several functionalities:
Automatic Monitoring: It will automatically monitor motions and sport changes. For instance, during nighttime, the sensor will detect the user's position when they are sleeping, while during the day, it will monitor their activities and suggest when they need to engage in more physical exercises.
Fall Detection: In the event of a fall, the device will send an alert signal to the corresponding contacts.
Data Storage and Connectivity: When motion data is generated, it will be saved onto an SD card for offline analysis if Bluetooth connectivity is unavailable. When there is Bluetooth connectivity, the collected data will be sent to a cloud server for real-time analysis.
Project Parts
The project is divided into three main parts:
- Motion Sensor Hardware Design
- Motion Sensor Software Design
- Motion Pattern Recognition
Motion Sensor Hardware Design
The hardware for the motion sensor is designed around the MMH H451 accelerometer and comprises two modules: one focused on Bluetooth and SD card storage and the other on Wi-Fi and cloud computing capabilities.
An accelerometer measures acceleration across the x, y, and z axes and has numerous daily applications, including game controllers and remote devices.
The prototype of the motion sensor with Wi-Fi functionality is complete, and the next steps involve minimizing both size and power consumption, with subsequent improvements to the user interface.
Motion Sensor with Wi-Fi
This sensor can send real-time accelerometer data to a cloud database via Wi-Fi. The user interface shows recorded accelerometer data, and the output is exported as a CSV file for Excel analysis.
Hardware Layout and Developments
The initial prototype was based on a Microchip development kit, which included a Wi-Fi module. However, due to significant power consumption, the Wi-Fi module has been replaced by Bluetooth.
Minor changes have been made to the circuit, incorporating a buzzer for alarming fall detection. Further attempts have merged the circuit components into a more compact whole, with capabilities to store raw data on an SD card and use USB mode for real-time graph display to a computer.
Software Design
In terms of software, I created a MATLAB function to plot output data. Although integrating acceleration data can theoretically track position, the practicality is less reliable due to error accumulation in double integration.
A C# program has also been developed to provide functionalities for filtering x, y, and z axes and enhancing motion pattern visibility. The motion pattern recognition process is still under development, with an aim to identify various motion patterns through software applications.
Motion Data Recognition
Different motion patterns have been analyzed, with specific attention to activities such as falling, jumping, running, and walking. The device is not only capable of fall detection but can also serve as a pedometer to motivate users toward increased physical activity.
Potentially significant applications include position tracking for underground workers or blind individuals, where GPS or Wi-Fi positioning data may not be available.
Demonstrations
The prototype can be connected to a computer via a USB port, displaying real-time sensor output and generating test documents for further plotting. The SD card functionality allows for saving motion data, while the Wi-Fi version enables data collection stored in a CSV file.
Hopefully, this project will demonstrate that even seemingly simple devices, like an accelerometer, can play a crucial role in health monitoring, alerting caregivers to emergencies and promoting exercise regimes for the elderly.
Keywords
- Health Monitoring
- Motion Sensor
- Fall Detection
- Elderly Individuals
- Accelerometer
- Bluetooth
- Wi-Fi
- Cloud Server
- Data Storage
- Motion Pattern Recognition
FAQ
Q1: What is the main objective of the health monitoring project?
A1: The main objective is to develop a wireless motion sensor that automatically monitors the movements and detects falls in elderly individuals.
Q2: How does the device alert for falls?
A2: When the sensor detects a fall, it sends an alert signal to designated contacts.
Q3: What types of data does the motion sensor collect?
A3: The motion sensor collects data related to movement patterns and physical activity, which can also be used for health reports.
Q4: How is the data stored if there is no Bluetooth connectivity?
A4: If Bluetooth is unavailable, the motion data is saved on an SD card for later analysis.
Q5: What are the two main modes of operation for the prototype?
A5: The prototype operates in two modes—SD card mode and USB (Bluetooth) mode, allowing for offline storage or real-time data transmission to a computer, respectively.
