TinyML DIY Fitness Tracker using Raspberry Pi Pico
TinyML DIY Fitness Tracker using Raspberry Pi Pico
The TinyML DIY Fitness Tracker using Raspberry Pi Pico is a miniature, low-power, and affordable IoT device designed to track various fitness metrics, including steps taken, distance traveled, and calories burned. This component combines the capabilities of Raspberry Pi Pico, a microcontroller board, with the power of TinyML, a lightweight machine learning framework, to create a compact and intelligent fitness tracker.
Raspberry Pi Pico
Accelerometer (3-axis), Gyroscope (3-axis)
TinyML
< 10mA (average), < 50mA (peak)
Up to 2 weeks (depending on usage and battery type)
Bluetooth 5.0, UART
40mm x 20mm x 10mm (approximate)
< 20g (approximate)
| The TinyML DIY Fitness Tracker using Raspberry Pi Pico is designed to be easily developed and deployed using widely available tools and programming languages, such as |
C, C++, MicroPython
Raspberry Pi Pico SDK, TinyML SDK, Visual Studio Code
Raspberry Pi Pico, Mobile devices (via Bluetooth connectivity)
This component is an excellent choice for developers, researchers, and enthusiasts looking to create innovative and affordable fitness tracking solutions using machine learning and IoT technologies.
TinyML DIY Fitness Tracker using Raspberry Pi PicoOverviewThe TinyML DIY Fitness Tracker using Raspberry Pi Pico is a compact, low-power, and highly customizable IoT component designed for wearable applications. This module integrates a Raspberry Pi Pico microcontroller, a 3-axis accelerometer, and a small LCD display, making it an ideal solution for building a DIY fitness tracker. The TinyML framework enables machine learning models to run directly on the device, allowing for real-time activity recognition and classification.Hardware ComponentsRaspberry Pi Pico microcontroller
3-axis accelerometer (e.g., ADXL345)
Small LCD display (e.g., SSD1306)
Lithium-polymer battery (e.g., 150mAh)
USB connector for charging and programmingSoftware ComponentsTinyML framework
MicroPython or C/C++ programming languages
Machine learning models (e.g., TensorFlow Lite, PyTorch)Code Examples### Example 1: Basic Accelerometer Reading and Activity RecognitionThis example demonstrates how to read accelerometer data and perform basic activity recognition using a pre-trained machine learning model.MicroPython Code
```python
import machine
import ueinference# Initialize accelerometer
accel = machine.Accelerometer(machine.Pin(16, machine.Pin.IN))# Load pre-trained machine learning model
model = ueinference.TFLiteModel("activity_recognition.tflite")while True:
# Read accelerometer data
x, y, z = accel.read()# Pre-process data
input_data = [(x, y, z)]# Run inference
output = model.run(input_data)# Get predicted activity
activity = output.index(max(output))# Display activity on LCD display
lcd = machine.LCD(0, 0, 128, 64)
lcd.fill(0)
lcd.text(str(activity), 0, 0)
lcd.show()
```
### Example 2: WiFi Connectivity and Cloud-based Data LoggingThis example demonstrates how to connect the TinyML DIY Fitness Tracker to a WiFi network and log activity data to a cloud-based platform using MQTT.C/C++ Code
```c
#include <WiFi.h>
#include <PubSubClient.h>
#include "tinyml.h"// WiFi credentials
const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";// MQTT server credentials
const char mqtt_server = "your_mqtt_server";
const char mqtt_username = "your_mqtt_username";
const char mqtt_password = "your_mqtt_password";WiFiClient espClient;
PubSubClient client(espClient);void setup() {
// Initialize WiFi and connect to network
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}
Serial.println("Connected to WiFi");// Initialize MQTT client
client.setServer(mqtt_server, 1883);
}void loop() {
// Read accelerometer data
float x, y, z;
read_accelerometer(&x, &y, &z);// Create JSON payload
char payload[256];
sprintf(payload, "{""activity"":""%d"