لوحة تطوير واي فاي UNO R4 ABX00087

Cricket Shot Recognition using Arduino UNO R4 WiFi + ADXL345 + Edge
دفعة
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

الخطوة 1: متطلبات الأجهزة

– Arduino UNO R4 WiFi
– ADXL345 Accelerometer (I2C)
– Jumper wires
– Breadboard (optional)
- كابل USB من النوع C

الخطوة 2: متطلبات البرامج

– 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 → 3.3 فولت
أرضي → أرضي
SDA → SDA (A4)
SCL → SCL (A5)
CS → 3.3V (optional, for I2C mode)
SDO → floating or GND

Step 4: Make IDE Sensor Ready

كيفية تثبيت مكتبات الاستشعار في Arduino IDE؟
افتح بيئة التطوير المتكاملة Arduino
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.
#يشمل
#include <Adafruit_ADXL345_U.h>
Adafruit_ADXL345_Unified accel =
Adafruit_ADXL345_Unified(12345);
إعداد فارغ () {
المسلسل.البداية(115200)؛
if (!accel.begin()) {
Serial.println(“No ADXL345 detected”);
بينما (1) ؛
}
accel.setRange(ADXL345_RANGE_4_G);
}
حلقة فارغة () {
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

1. Close Arduino Serial Monitor.
2. Run the command: edge-impulse-data-forwarder –frequency 18
3. Enter axis names: accX, accY, accZ
4. Name your device: Arduino-Cricket-Board
5. Confirm connection in Edge Impulse Studio under ‘Devices’.

الخطوة 7: جمع البيانات

In Edge Impulse Studio → Data acquisition:
– Device: Arduino-Cricket-Board
– Sensor: Accelerometer (3 axes)
– سample length: 2000 ms (2 seconds)
– التردد: 18 هرتز
Record at least 40 samples per class:
– Cover Drive
– Straight Drive
– Pull ShotCollect Data Exampليه
غطاء محرك السيارة
Device: Arduino-Cricket-Board
Label: Cover Drive
Sensor: Sensor with 3 axes (accX, accY, accZ)
Sample length: 10000ms
التردد: 18 هرتز
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
التردد: 18 هرتز
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
التردد: 18 هرتز
Example Raw Data:
accX 2.01
accY 7.84
accZ -0.63

Step 8: Impulse Design

Open Create impulse:
كتلة الإدخال: بيانات السلسلة الزمنية (3 محاور).
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.
كتلة التعلم: التصنيف (كيراس).
انقر فوق حفظ الدافع.

Generate features:
انتقل إلى التحليل الطيفي، وانقر فوق حفظ المعلمات، ثم إنشاء ميزات لمجموعة التدريب.

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).
قم بتمكين EON Compiler (إذا كان متاحًا) لتقليص حجم النموذج. Download the .zip, then in Arduino IDE: Sketch → Include Library → Add .ZIP Library… This adds exampمثل المخزن المؤقت الثابت والمستمر تحت File → مثالampليه →
Your Project Name – Edge Impulse. Inference sketch for Arduino UNO EK R4 WiFi + ADXL345.

Step 10: Arduino Inference Sketch

#يشمل
#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;
إعداد فارغ () {
المسلسل.البداية(115200)؛
while (!Serial) {}
if (!accel.begin()) {
Serial.println(“ERROR: ADXL345 not detected”);
بينما (1) ؛
}
accel.setRange(ADXL345_RANGE_4_G);
}
حلقة فارغة () {
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);
#نهاية_الحياة
ei_printf(“\n”);
}

الإخراج السابقampعلى:

نصائح:
حافظ على مزامنة EI_CLASSIFIER_INTERVAL_MS مع تردد مُرسِل البيانات (مثلاً، ١٠٠ هرتز → ١٠ مللي ثانية). تُعيّن مكتبة Edge Impulse هذا الثابت تلقائيًا من نبضتك.
إذا كنت تريد الكشف المستمر (نافذة منزلقة)، فابدأ من النافذة المستمرةampتم تضمين le مع مكتبة EI ويتم التبديل في قراءات ADXL345.
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

المستندات / الموارد

Arduino ABX00087 UNO R4 WiFi Development Board [بي دي اف] دليل المستخدم R4 WiFi, ADXL345, ABX00087 UNO R4 WiFi Development Board, ABX00087, UNO R4 WiFi Development Board, WiFi Development Board, Development Board, Board

مراجع

• دليل المستخدم