Microwave Radar Human Body Sensor
Microwave Radar Human Body Sensor
The Microwave Radar Human Body Sensor is a cutting-edge Internet of Things (IoT) component designed to detect and track human presence and movement using microwave radar technology. This sensor is ideal for applications requiring accurate and real-time human presence detection, such as smart home automation, security systems, and healthcare monitoring.
The Microwave Radar Human Body Sensor operates by transmitting low-power microwave signals and measuring the reflected signals that bounce back from the environment and any objects within its detection range. The sensor utilizes the Doppler effect principle to differentiate between stationary and moving objects, enabling it to accurately detect human presence and movement.
Detects human presence within the detection range.
Detects human movement within the detection range.
Measures the distance of the detected object from the sensor.
Up to 10 meters
120
1 cm
Up to 100 Hz
3.3V to 5V
<50mA
Digital (UART, I2C, SPI)
SMD orThrough-Hole
25mm x 25mm x 10mm (SMD), 30mm x 30mm x 15mm (Through-Hole)
The Microwave Radar Human Body Sensor is a cutting-edge IoT component that provides accurate and real-time human presence detection and tracking. Its low power consumption, compact design, and multi-mode operation make it an ideal solution for a wide range of applications, including smart home automation, security systems, healthcare monitoring, and industrial automation.
Microwave Radar Human Body Sensor DocumentationOverviewThe Microwave Radar Human Body Sensor is a non-invasive, low-power sensor capable of detecting the presence and movement of humans within a range of 1-10 meters. It operates by emitting microwave signals and measuring the reflections to determine the presence of a human body. This sensor is ideal for applications such as smart home automation, security systems, and health monitoring.Technical SpecificationsFrequency: 24 GHz
Range: 1-10 meters
Resolution: 10 cm
Power consumption: 20 mA (typical), 50 mA (max)
Communication interface: I2C, SPI
Operating temperature: -20C to 70C
Size: 20 mm x 20 mm x 5 mmCode Examples### Example 1: Basic Detection using ArduinoThis example demonstrates how to use the Microwave Radar Human Body Sensor with an Arduino board to detect human presence.```c++
#include <Wire.h>#define SENSOR_ADDRESS 0x5Avoid setup() {
Serial.begin(9600);
Wire.begin();
}void loop() {
byte data;
Wire.beginTransmission(SENSOR_ADDRESS);
Wire.write(0x00); // Register address for detection data
Wire.endTransmission();
Wire.requestFrom(SENSOR_ADDRESS, 1);
data = Wire.read();
if (data > 0) {
Serial.println("Human detected!");
} else {
Serial.println("No human detected.");
}
delay(500);
}
```### Example 2: Gesture Recognition using Raspberry Pi (Python)This example demonstrates how to use the Microwave Radar Human Body Sensor with a Raspberry Pi to recognize basic hand gestures (e.g., waving, clapping).```python
import RPi.GPIO as GPIO
import time
import numpy as np# Set up GPIO pins for SPI communication
GPIO.setmode(GPIO.BCM)
GPIO.setup(18, GPIO.OUT) # SCK
GPIO.setup(23, GPIO.OUT) # MOSI
GPIO.setup(24, GPIO.IN) # MISO
GPIO.setup(25, GPIO.OUT) # CSdef read_sensor():
# Send SPI command to read sensor data
GPIO.output(25, GPIO.LOW)
GPIO.output(18, GPIO.HIGH)
GPIO.output(23, GPIO.HIGH)
time.sleep(0.01)
GPIO.output(23, GPIO.LOW)
time.sleep(0.01)
data = []
for i in range(16):
GPIO.output(18, GPIO.HIGH)
bit = GPIO.input(24)
data.append(bit)
GPIO.output(18, GPIO.LOW)
GPIO.output(25, GPIO.HIGH)
return np.array(data)def recognize_gesture(data):
# Simple gesture recognition using peak detection
peaks = np.where(np.diff(np.sign(np.diff(data))) < 0)[0] + 1
if len(peaks) > 2:
print("Waving detected!")
elif len(peaks) == 2:
print("Clapping detected!")
else:
print("No gesture detected.")while True:
data = read_sensor()
recognize_gesture(data)
time.sleep(0.1)
```Note: In both examples, you will need to modify the sensor address, pin connections, and communication protocols according to your specific setup. Additionally, the gesture recognition example is a basic implementation and may require further development and calibration for accurate results.