firmware-arduino

name	firmware-arduino
description	Write Arduino application code that uses a downloaded Edge Impulse library. Use when asked to write a sketch, integrate an Edge Impulse model into Arduino code, or run inference on an Arduino board.
metadata	{"version":"1.0.1"}

Write Arduino sketches that use a downloaded Edge Impulse Arduino library.

Library structure

The downloaded .zip contains an Arduino library. The main header follows the pattern:

#include <PROJECT_SLUG_inferencing.h>

where PROJECT_SLUG is derived from the project name. Find the exact header name by listing the extracted library directory:

find ./build -name '*_inferencing.h' 2>/dev/null | head -1

Use whatever filename that command returns — do not guess or hardcode it.

Inference pattern

#include <PROJECT_SLUG_inferencing.h>   // use the actual filename found above

float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE];

int raw_feature_get_data(size_t offset, size_t length, float *out_ptr) {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
}

void loop() {
    // Fill features[] with sensor readings here

    signal_t signal;
    numpy::signal_from_buffer(features, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);

    ei_impulse_result_t result;
    EI_IMPULSE_ERROR err = run_classifier(&signal, &result, false);
    if (err != EI_IMPULSE_OK) {
        Serial.print("run_classifier failed: "); Serial.println(err);
        return;
    }

    for (size_t i = 0; i < EI_CLASSIFIER_LABEL_COUNT; i++) {
        Serial.print(result.classification[i].label);
        Serial.print(": ");
        Serial.println(result.classification[i].value, 4);
    }
}

Key constants defined by the library

• EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE — total floats needed for one inference window
• EI_CLASSIFIER_LABEL_COUNT — number of output classes
• EI_CLASSIFIER_INTERVAL_MS — sampling interval in milliseconds

Instructions

1. Before writing any code, run find ./build -name '*_inferencing.h' 2>/dev/null | head -1 to find the exact header filename. Use that name in the #include — do not guess.
2. Use EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE for buffer sizing — never hardcode the number.
3. Collect sensor data at EI_CLASSIFIER_INTERVAL_MS intervals.
4. Ask the user which sensor they are using (IMU, microphone, or camera) if not specified.
5. Use the Arduino Wire, SPI, or sensor-specific libraries appropriate for the board.

Documentation

• https://docs.edgeimpulse.com/hardware/deployments/run-arduino-2-0 — run an Arduino library deployment with the Arduino IDE

Reference repositories

Official Edge Impulse Arduino firmware — complete, board-specific ingestion and inferencing implementations to reference for sensor integration patterns:

• https://github.com/edgeimpulse/firmware-arduino-nano-33-ble-sense — Nano 33 BLE Sense (IMU, microphone)
• https://github.com/edgeimpulse/firmware-arduino-nicla-vision — Nicla Vision (camera)
• https://github.com/edgeimpulse/firmware-arduino-nicla-voice — Nicla Voice (audio, Syntiant NDP)
• https://github.com/edgeimpulse/firmware-arduino-portenta-h7 — Portenta H7 (camera)

Application sketch examples:

• https://github.com/edgeimpulse/example-nesso-n1-smart-door-sensor — accelerometer ingestion and inference on the Arduino Nesso N1 over WiFi and Zigbee, using deep sleep
• https://github.com/edgeimpulse/example-portenta-lorawan — computer vision over LoRaWAN with the Portenta H7