Wheel-chair fall detection and protection

Wheel chair fall detection and protection

For my final year project, my role focused on the circuit design, simulation, and connection aspects of the system. The project involved developing a wheelchair fall detection system, and the circuit design, as shown below, incorporates various components such as an Arduino Uno, an ADXL335 accelerometer for detecting tilts or falls, a GPS module for location tracking, a GSM SIM 900 module for sending emergency alerts, and an ESP8266 WiFi module for remote monitoring. I meticulously designed the circuit to ensure seamless integration of these components, simulated their operation to validate the system's functionality, and carried out the physical connections to bring the design to life. This work was crucial for ensuring that the system could detect falls accurately and send alerts efficiently, contributing significantly to the project's overall success.

in abaove circiut desgin in cirkit designer website in that we create desgin of any arduino iot projects

The circuit diagram illustrates a system designed for wheelchair fall detection using an Arduino Uno microcontroller. The primary components include an accelerometer sensor (ADXL335), a GPS module (NEO 6M), a GSM SIM 900 module, an ESP8266 WiFi module, a buzzer, servo motors (MG996R), and a rechargeable battery for power. Each component is interconnected with the Arduino, and the system is powered using the battery module. The system detects unusual tilts or falls based on accelerometer readings and alerts a caregiver or monitoring system.

How It Works:

1. Accelerometer for Fall Detection: The ADXL335 accelerometer sensor measures the wheelchair's orientation in three axes (X, Y, and Z). If the readings indicate abnormal tilting or falling (exceeding predefined thresholds), it triggers an alert. These readings are sent to the Arduino for processing.
2. Alert Mechanism: Once a fall is detected, the Arduino activates a buzzer for an immediate audible alert. Simultaneously, the GSM SIM 900 module sends an SMS alert to predefined emergency contacts, including the location obtained from the GPS module.
3. Location Tracking: The GPS NEO 6M module provides the exact geographical coordinates of the wheelchair's position. This data is crucial for rescuers to locate the individual quickly. The location information is sent along with the SMS alert.
4. Remote Monitoring via WiFi: The ESP8266 WiFi module enables the system to send fall detection data to a remote server or mobile application for real-time monitoring. This feature ensures caregivers can access updates and take action remotely.
5. Additional Safety Features: The servo motors (MG996R) may be used for mechanical adjustments, such as stabilizing the wheelchair or locking it in place after a fall. The rechargeable battery ensures continuous operation without reliance on external power sources.

Simulation:

To simulate this setup:

1. Connect the components as per the circuit diagram using jumper wires and a breadboard.
2. Upload the Arduino code that handles sensor data, GSM communication, GPS data reading, and WiFi data transmission. The code can be tested with the Arduino IDE.
3. Simulate falls by tilting the accelerometer and observe the buzzer, SMS alerts, and data updates on the remote monitoring platform.
4. Ensure proper power connections from the rechargeable battery and validate each module's functionality individually before integrating the entire system.
5. Test the system in different conditions to confirm accuracy, such as normal wheelchair movement and actual falls.

Would you like help with the Arduino code or component setup?

how the accelerometer sensor detect the fall:

in above image is the shows the accelerometer has three-dimensional space (X, Y, and Z axes) these changes get detection of the fall

1. Static Orientation Detection: The accelerometer senses the gravitational force (1g) along its axes. For instance, in position (a), the force is aligned entirely along the Z-axis, indicating the device is flat on a surface. This serves as the baseline state for normal wheelchair positioning.

2. Tilt and Angle Detection: When the accelerometer tilts, such as in position (b), the gravitational force is distributed among the axes (X, Y, and Z). The angle of tilt can be calculated using trigonometric functions based on the sensor's output values. This change indicates a deviation from the baseline orientation.

3. Dynamic Movement: Positions (c) and (d) show scenarios where the accelerometer experiences forces due to sudden tilts or falls. For example, if the wheelchair tips forward or sideways, the sensor detects a rapid shift in force across the axes. These abrupt changes in acceleration patterns are used to identify potential falls.

4. Threshold-Based Detection: In a fall detection system, thresholds are set for accelerometer readings to differentiate between normal movements (e.g., tilting or vibrations) and abnormal events (e.g., falling). If the force along one axis drops significantly (e.g., close to zero), while another axis shows a sharp spike, it signals a fall event.

5. Integration with System: The accelerometer outputs data in terms of voltage changes, which are interpreted by the microcontroller (Arduino). The Arduino processes the data to compare against predefined thresholds. When a fall is detected, it triggers alerts and other safety mechanisms, such as activating a buzzer, sending an SMS via GSM, or logging the event on a monitoring platform via WiFi.

6. This wheel chair Fall Detection and Protection System offers a reliable solution to enhanced the safety of wheel chair users. By providing immediate alterts during fall incidents, it ensures timely assistance there by reducing injuries and improving overall user confidence.

7. For a det

connections part : 

After completing all the connections, we place the setup inside a box :

Conclusion:

This wheel chair Fall Detection and Protection System offers a reliable solution to enhanced the safety of wheel chair users. By providing immediate alterts during fall incidents, it ensures timely assistance there by reducing injuries and improving overall user confidence.

For a detailed walkthrough of the project, including the circuit diagram and source code, please visit the GitHub repository