ABX00087 UNO R4 WiFi izstrādes plate
Cricket Shot Recognition using Arduino UNO R4 WiFi + ADXL345 + Edge
Impulss
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. darbība. Aparatūras prasības
– Arduino UNO R4 WiFi
– ADXL345 Accelerometer (I2C)
– Jumper wires
– Breadboard (optional)
– C tipa USB kabelis
2. darbība: programmatūras prasības
– 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 V
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
Kā instalēt sensoru bibliotēkas Arduino IDE?
Atvērt Arduino IDE
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.
#iekļauts
#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”);
kamēr (1);
}
accel.setRange(ADXL345_RANGE_4_G);
}
void loop() {
sensors_event_t e;
accel.getEvent(&e);
Serial.print (e.acceleration.x);
Sērijas.drukāt(“,”);
Serial.print(e.acceleration.y);
Sērijas.drukāt(“,”);
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. solis: datu vākšana
In Edge Impulse Studio → Data acquisition:
– Device: Arduino-Cricket-Board
– Sensor: Accelerometer (3 axes)
– Sample length: 2000 ms (2 seconds)
– Frekvence: 18 Hz
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
Frekvence: 18 Hz
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
Frekvence: 18 Hz
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
Frekvence: 18 Hz
Example Raw Data:
accX 2.01
accY 7.84
accZ -0.63 
Step 8: Impulse Design
Open Create impulse:
Ievades bloks: Laikrindu dati (3 asis).
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.
Mācību bloks: Klasifikācija (Keras).
Noklikšķiniet uz Saglabāt impulsu. 
Generate features:
Dodieties uz Spektrālā analīze, noklikšķiniet uz Saglabāt parametrus un pēc tam Ģenerēt apmācības kopas funkcijas.
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).
Iespējojiet EON kompilatoru (ja pieejams), lai samazinātu modeļa izmēru. 
Download the .zip, then in Arduino IDE: Sketch → Include Library → Add .ZIP Library… This adds examppiemēram, statiskā bufera un nepārtrauktā režīma File → Piemamples →
Your Project Name – Edge Impulse. Inference sketch for Arduino UNO EK R4 WiFi + ADXL345.
Step 10: Arduino Inference Sketch
#iekļauts
#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”);
kamēr (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”);
}
Izvade, piemample:
Padomi:
Saglabājiet EI_CLASSIFIER_INTERVAL_MS sinhronizācijā ar datu pārsūtīšanas ierīces frekvenci (piemēram, 100 Hz → 10 ms). Edge Impulse bibliotēka automātiski iestata šo konstanti no jūsu impulsa.
Ja vēlaties nepārtrauktu noteikšanu (slīdošais logs), sāciet ar nepārtrauktās noteikšanas režīmu.ampfails ir iekļauts EI bibliotēkā un tiek mainīts ADXL345 lasījumos.
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
Dokumenti / Resursi
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