Raspberry Clock

Conventional Pomodoro timers are often just passive countdown tools with limited utility. In this project, I leveraged the Raspberry Pi 5 to transform a simple timer into an active, intelligent monitoring station. By fusing YOLOv8 computer vision with a C1001 24GHz mmWave radar, the system does more than just track time—it understands the user’s state. It monitors both physical presence and vital signs (heart rate and respiration) in a completely non-contact manner. Through a small integrated display, the device provides real-time status feedback, serving as both a productivity coach and a health guardian that alerts the user when it’s time to focus or rest.(P.S. as a non-native speaker, I used Gemini to polish this tutorial)

Supplies

Raspberry Pi 5 (4GB)
C1001 60GHz mmWave Indoor Fall Detection Sensor
IMX219 Camera Module
0.96Inch IPS Screen OLED Display Module
a little electronic materials

Connect Camera

Hardware: Connect the IMX219 Camera Module to the Pi 5's CSI port using the dedicated adapter cable.

Download: Install the YOLOv8 environment. Open the terminal and run:

pip install ultralytics

System Configuration

To ensure stable communication with the radar, we must release the hardware UART:

Disable Serial Console: Run sudo raspi-config, navigate to Interface Options > Serial, select No for login shell and Yes for hardware port.
Disable Bluetooth: Open the configuration file:

sudo nano /boot/firmware/config.txt

Add this line at the end: dtoverlay=disable-bt.

Reboot: Restart the Pi to apply changes.

Radar Connection & Library Download

Hardware: Connect C1001 Radar pins: VCC to 5V, GND to GND, TX to GPIO 15 (RXD0), and RX to GPIO 14 (TXD0).

Download: Clone the official driver library into your project folder:

git clone https://github.com/DFRobot/DFRobot_HumanDetection.git

cp DFRobot_HumanDetection/python/raspberrypi/DFRobot_HumanDetection.py ./

Screen Integration & Final Run

Hardware: Connect the ST7735 LCD via SPI:

SCK (GPIO 11), MOSI (GPIO 10), CS (GPIO 8), DC (GPIO 24), RES (GPIO 25).

Execution: Integrate the camera and radar modules into a multi-threaded Python script. Run the program and remain still for 60 seconds during the initial calibration to lock onto the vital signs.

Test

When the camera detects a person, it will display green, and will work with radar to detect heart rate and breathing rate; when the user picks up the phone, the small screen will turn red to warn the user.