ABX00087 UNO R4 WiFi өнүктүрүү кеңеши
Cricket Shot Recognition using Arduino UNO R4 WiFi + ADXL345 + Edge
Импульс
This document provides a complete workflow for building a cricket shot recognition system using Arduino UNO R4 WiFi with an ADXL345 accelerometer and Edge Impulse Studio. The project involves collecting accelerometer data, training a machine learning model, and deploying the trained model back to the Arduino for real-time shot classification.
Cricket shots considered in this project:
– Cover Drive
– Straight Drive
– Pull Shot
1-кадам: Аппараттык талаптар
– Arduino UNO R4 WiFi
– ADXL345 Accelerometer (I2C)
– Jumper wires
– Breadboard (optional)
– USB Type-C кабели
2-кадам: Программага талаптар
– Arduino IDE (latest)
– Edge Impulse Studio account (free)
– Edge Impulse CLI tools (Node.js required)
– Adafruit ADXL345 library
Step 3: Wiring the ADXL345
Connect the ADXL345 sensor to the Arduino UNO R4 WiFi as follows:
VCC → 3.3V
GND → GND
SDA → SDA (A4)
SCL → SCL (A5)
CS → 3.3V (optional, for I2C mode)
SDO → floating or GND
Step 4: Make IDE Sensor Ready
Arduino IDEге сенсордук китепканаларды кантип орнотуу керек?
Arduino IDE ачыңыз
Open Tools → Manage Libraries… and install: Adafruit ADXL345 Unified Adafruit Unified Sensor
(If you have LSM6DSO or MPU6050 instead: install SparkFun LSM6DSO , Adafruit LSM6DS or MPU6050 accordingly.)
Step 5: Arduino Sketch for Data Collection
Upload this sketch to your Arduino UNO R4 WiFi. It streams accelerometer data in CSV format (x,y,z) at ~18 Hz for Edge Impulse.
#киргизүү
#include <Adafruit_ADXL345_U.h>
Adafruit_ADXL345_Unified accel =
Adafruit_ADXL345_Unified(12345);
void setup() {
Serial.begin(115200);
if (!accel.begin()) {
Serial.println(“No ADXL345 detected”);
while (1);
}
accel.setRange(ADXL345_RANGE_4_G);
}
void loop() {
sensors_event_t e;
accel.getEvent(&e);
Serial.print (e.acceleration.x);
Serial.print(“,”);
Serial.print(e.acceleration.y);
Serial.print(“,”);
Serial.println(e.acceleration.z);delay(55); // ~18 Hz
}
Set Up Edge Impulse
Step 6: Connecting to Edge Impulse
- Close Arduino Serial Monitor.
- Run the command: edge-impulse-data-forwarder –frequency 18
- Enter axis names: accX, accY, accZ
- Name your device: Arduino-Cricket-Board
- Confirm connection in Edge Impulse Studio under ‘Devices’.
7-кадам: Маалыматтарды чогултуу
In Edge Impulse Studio → Data acquisition:
– Device: Arduino-Cricket-Board
– Sensor: Accelerometer (3 axes)
– Сample length: 2000 ms (2 seconds)
– Жыштык: 18 Гц
Record at least 40 samples per class:
– Cover Drive
– Straight Drive
– Pull Shot
Collect Data Examples
Cover Drive
Device: Arduino-Cricket-Board
Label: Cover Drive
Sensor: Sensor with 3 axes (accX, accY, accZ)
Sample length: 10000ms
Жыштык: 18 Гц
Example Raw Data:
accX -0.32
accY 9.61
accZ -0.12
Straight Drive
Device: Arduino-Cricket-Board
Label: Straight Drive
Sensor: Sensor with 3 axes (accX, accY, accZ)
Sample length: 10000ms
Жыштык: 18 Гц
Example Raw Data:
accX 1.24
accY 8.93
accZ -0.42
Pull Shot
Device: Arduino-Cricket-Board
Label: Pull Shot
Sensor: Sensor with 3 axes (accX, accY, accZ)
Sample length:10000 ms
Жыштык: 18 Гц
Example Raw Data:
accX 2.01
accY 7.84
accZ -0.63 
Step 8: Impulse Design
Open Create impulse:
Киргизүү блогу: Убакыт сериясынын маалыматтары (3 ок).
Window size: 1000 ms Window increase (stride): 200 ms Enable: Axes, Magnitude (optional), frequency 18.
Processing block: Spectral analysis (a.k.a. Spectral Features for motion). Window size: 1000 ms Window increase (stride): 200 ms Enable: Axes, Magnitude (optional), keep all defaults first.
Окуу блогу: Классификация (Керас).
Сактоо импульсун чыкылдатыңыз. 
Generate features:
Спектралдык анализге өтүп, Сактоо параметрлерин чыкылдатыңыз, андан кийин машыгуу топтому үчүн функцияларды жаратыңыз.
Train a small model
Go to Classifier (Keras) and use a compact config like:
Neural network: 1–2 dense layers (e.g., 60 → 30), ReLU
Epochs: 40–60
Learning rate: 0.001–0.005
Batch size: 32
Data split: 80/20 (train/test)
Save and train the data
Evaluate and Check Model testing with the holdout set.
Inspect the confusion matrix; if circle and up overlap, collect more diverse data or tweak
Spectral parameters (window size / noise floor).
Step 9: Deployment to Arduino
Go to Deployment:
Choose Arduino library (C++ library also works).
Моделдин өлчөмүн кичирейтүү үчүн EON Compiler (эгер бар болсо) иштетиңиз. 
Download the .zip, then in Arduino IDE: Sketch → Include Library → Add .ZIP Library… This adds exampStatic buffer жана Continuous under сыяктуу File → Мисamples →
Your Project Name – Edge Impulse. Inference sketch for Arduino UNO EK R4 WiFi + ADXL345.
Step 10: Arduino Inference Sketch
#киргизүү
#include <Adafruit_ADXL345_Unified.h>
#include <your_project_inference.h> // Replace with Edge Impulse header
Adafruit_ADXL345_Unified accel =
Adafruit_ADXL345_Unified(12345);
static bool debug_nn = false;
void setup() {
Serial.begin(115200);
while (!Serial) {}
if (!accel.begin()) {
Serial.println(“ERROR: ADXL345 not detected”);
while (1);
}
accel.setRange(ADXL345_RANGE_4_G);
}
void loop() {
float buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = {0};
for (size_t ix = 0; ix < EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE; ix +=
3) {
uint64_t next_tick = micros() + (EI_CLASSIFIER_INTERVAL_MS *
1000);
sensors_event_t e;
accel.getEvent(&e);
buffer[ix + 0] = e.acceleration.x;
buffer[ix + 1] = e.acceleration.y;
buffer[ix + 2] = e.acceleration.z;
int32_t wait = (int32_t)(next_tick – micros());
if (wait > 0) delayMicroseconds(wait);
}
signal_t signal;
int err = numpy::signal_from_buffer(buffer,
EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);
if (err != 0) return;
ei_impulse_result_t result = {0};
EI_IMPULSE_ERROR res = run_classifier(&signal, &result,
debug_nn);
if (res != EI_IMPULSE_OK) return;
for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
ei_printf(“%s: %.3f “, result.classification[ix].label,
result.classification[ix].value);
}
#if EI_CLASSIFIER_HAS_ANOMALY == 1
ei_printf(“anomaly: %.3f”, result.anomaly);
#endif
ei_printf(“\n”);
}
Чыгуу эксampле:
Кеңештер:
EI_CLASSIFIER_INTERVAL_MSти маалымат экспедитор жыштыгыңыз менен шайкештештириңиз (мисалы, 100 Гц → 10 мс). Edge Impulse китепканасы бул константты сиздин импульсуңуздан автоматтык түрдө орнотот.
Эгер сиз үзгүлтүксүз аныктоону кааласаңыз (жылжымалы терезе), Үзгүлтүксүз мурункудан баштаңызample EI китепканасы менен камтылган жана ADXL345 окууларында алмашуу.
We will be adding video tutorials soon; till then, stay tuned – https://www.youtube.com/@RobuInlabs
And If you still have some doubts, you can check out this video by Edged Impulse: https://www.youtube.com/watch?v=FseGCn-oBA0&t=468s
Документтер / Ресурстар
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Arduino ABX00087 UNO R4 WiFi Development Board [pdf] Колдонуучунун колдонмосу R4 WiFi, ADXL345, ABX00087 UNO R4 WiFi Development Board, ABX00087, UNO R4 WiFi Development Board, WiFi Development Board, Development Board, Board |