Advancing Fall Detection: Crafting a Custom PCB for the Raspberry Pi Zero 2W
Schematics of the PCB module
In my previous post, Developing a Fall Detection Device with Raspberry Pi, I delved into the initial steps of building a fall detection system using the Raspberry Pi Zero 2W. Today, I’m excited to share the next phase of this journey: designing and fabricating a custom PCB that integrates essential sensors to create a compact, robust, and efficient fall detection device.
The Quest for Compactness
The primary goal of this project was to shrink the device’s size, making it thinner and more wearable for our target users. Bulky prototypes are a hindrance, especially when designing for wearables that need to be unobtrusive yet reliable. By creating a custom PCB tailored to the Raspberry Pi Zero 2W, I aimed to eliminate the tangle of wires and breadboards, resulting in a sleek, sturdy unit.
Sensor Selection: More Than Just Data Points
Choosing the right sensors was crucial. The device needed to accurately detect falls by monitoring various physiological and environmental parameters. Here’s what made the cut:
- Accelerometer: Captures sudden changes in acceleration, a key indicator of falls.
- Gyroscope: Detects deviations in orientation and angular velocity.
- Barometer: Measures pressure changes to calculate shifts in altitude.
- ECG (Electrocardiogram): Monitors heart rate for sudden changes or arrhythmias that often accompany falls.
Integrating ECG data with motion sensors adds a physiological layer to the detection algorithm, enhancing accuracy by providing context to motion events.
Designing the PCB: Challenges and Triumphs
Squeezing Components onto a Small Canvas
One of the main challenges was fitting all these sensors onto a PCB that matches the compact dimensions of the Raspberry Pi Zero 2W. Space was at a premium, and efficient layout planning was essential.
From Schematic to Reality
I designed the circuit from scratch using Altium Designer, a powerful PCB and schematic design tool. Altium’s built-in compiler and DFM manager were invaluable, ensuring all connections were reliable and met industry standards. The schematics were meticulously drafted, referencing the datasheets and reference circuits of each sensor to ensure accuracy.
Power Management
Powering the Raspberry Pi and the array of sensors required careful consideration. The Raspberry Pi Zero 2W draws about 5W, necessitating a voltage booster when using battery power. However, rather than adding discrete voltage regulators, I leveraged the Pi’s onboard voltage regulation. A 5V supply from the battery feeds the Pi, which then regulates and supplies 3.3V to the connected sensors.
Prototyping and Testing: Ironing Out the Kinks
3D render of the PCB module
After receiving the fabricated PCB, I dove into testing:
- Sensor Performance: Each sensor was interfaced via the I2C protocol. Data readings confirmed they were functioning correctly.
- Integration Testing: Running all sensors simultaneously in inference mode, I deployed a machine learning model to detect falls. Impressively, the system could detect a fall within 8 seconds—most of which was data collection time. The actual detection algorithm executed in mere milliseconds.
- Real-world Application: Although formal environmental testing wasn’t conducted, the device performed reliably during simulated falls, proving the concept’s viability.
The Road Ahead: Enhancing Detection with ECG Data
The next milestone is integrating ECG data into the machine learning model. By combining motion data with heart rate patterns, we can reduce detection time and increase accuracy. My goal is to cut the detection window down to 4 seconds, making the system even more responsive.
Impact and Community Involvement
If I open-source this PCB design, it could empower the maker community to develop affordable fall detection devices, potentially benefiting the elderly and those with medical conditions prone to falls. I’m keen on collaborating with like-minded individuals passionate about leveraging technology for health monitoring.
Final Thoughts
Designing this PCB was a rewarding challenge. Sending the design off for fabrication and holding the finished product was a gratifying culmination of meticulous planning and problem-solving. This project not only pushes the boundaries of fall detection technology but also exemplifies how custom hardware solutions can enhance the functionality of compact computing platforms like the Raspberry Pi Zero 2W.
Stay tuned for future updates, where I’ll delve into the integration of ECG data and share more about optimizing the machine learning model for faster detection times.