--- title: "TinyML-Based Object Drop Classifier Using MPU6050 and W55RP20-EVB-Pico" url: "https://maker.wiznet.io/Hannah/projects/tinyml-based-object-drop-classifier-using-mpu6050-and-w55rp20-evb-pico/" markdown_url: "https://maker.wiznet.io/Hannah/projects/tinyml-based-object-drop-classifier-using-mpu6050-and-w55rp20-evb-pico/md" type: "WCC: WIZnet Created Content" author: "Hannah Lee" editor: "WIZnet" editor_url: "https://maker.wiznet.io/" original_author: "Hannah Lee" published: "2025-04-01" language: "en" tags: ["W55RP20", "Arduino IDE", "AIoT", "Machine Learning & AI"] hardware: ["WIZnet W55RP20-EVB-Pico"] likes: 0 views: 1445 comments: 0 source: "WIZnet Makers (https://maker.wiznet.io/)" --- # TinyML-Based Object Drop Classifier Using MPU6050 and W55RP20-EVB-Pico > Deploying a Fall Detection ML Model on W5500-EVB-Pico Using Edge Impulse Original author: Hannah Lee ## Components - **WIZnet W55RP20-EVB-Pico** x 1 ([docs](https://wiznet.io/products/powered-by-raspberry-pi/w55rp20-evb-pico)) - Software: **Arduino IDE** ([docs](https://www.arduino.cc/en/software)) - Software: **Edge Impulse** ([docs](https://docs.edgeimpulse.com/docs)) ## Article #### Introduction Fall and object drop detection is a critical aspect of safety in industrial environments and for elderly individuals. This project demonstrates how to build a machine learning-based system using the **MPU6050 6-axis IMU sensor**, **Edge Impulse platform**, and the **WIZnet W55RP20-EVB-Pico (RP2040-based)** board to detect and classify fall or object drop events. This guide walks you through the entire development process—from sensor data collection to model training and deployment on a microcontroller. #### Core Hardware & Technology | Component | Description | | --- | --- | | **W55RP20-EVB-Pico** | RP2040 MCU integrated with W5500 Ethernet chip | | **MPU6050** | 3-axis accelerometer + 3-axis gyroscope IMU sensor | | **Edge Impulse** | TinyML platform for embedded machine learning model training and deployment | | **Arduino IDE** | Environment for firmware development and deployment | #### Implementation ##### ✅ Data Collection [**How to setup Pico as Edge device with Edge Impulse**](https://maker.wiznet.io/lawrence/projects/how%2Dto%2Dsetup%2Dpico%2Das%2Dedge%2Ddevice%2Dwith%2Dedge%2Dimpulse/) ## [**Motion recognition + anomaly detection Guide**](https://docs.edgeimpulse.com/docs/tutorials/end-to-end-tutorials/time-series/continuous-motion-recognition) Used Edge Impulse's **Data Forwarder** to collect labeled sensor data for `drop` and `idle` states from the MPU6050 Data was streamed in real time over Serial and labeled within the Edge Impulse Studio ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743429445%2Epng) ##### ✅ Model Training Signal processing: Spectral features Classifier: Dense Neural Network Target labels: `drop`, `idle` ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743439210%2Epng) ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743439265%2Epng) ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743439314%2Epng) ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743439372%2Epng) ##### ✅ Device Deployment and Inference Trained model exported as an **Arduino library (.zip)** for deployment on RP2040 Real-time inference executed using the `run_classifier()` function Serial output triggered on detecting `drop`, with support for event hooks ![](https://maker.wiznet.io/upload/ckeditor5/244562842%5F1743439462%2Epng) ✅ **WIZnet W55RP20-EVB-Pico Arduino build guide** [**>> How to Build W55RP20-EVB-Pico on Arduino**](https://maker.wiznet.io/mason/projects/how%2Dto%2Dbuild%2Dw55rp20%2Devb%2Dpico%2Don%2Darduino/?page=3&serob=rt&serterm=year) ##### 🧠 Code Logic ```c #include #include #include #define CONVERT_G_TO_MS2 9.80665f #define FREQUENCY_HZ EI_CLASSIFIER_FREQUENCY #define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) MPU6050 accelgyro; int16_t ax, ay, az; static unsigned long last_interval_ms = 0; float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE]; size_t feature_ix = 0; void setup() { Serial.begin(115200); while (!Serial); Serial.println("[START] Drop Detection with Edge Impulse + MPU6050"); Wire.begin(); accelgyro.initialize(); if (!accelgyro.testConnection()) { Serial.println("MPU6050 connection failed!"); while (1); } Serial.println("MPU6050 connected."); run_classifier_init(); } void loop() { if (millis() > last_interval_ms + INTERVAL_MS) { last_interval_ms = millis(); accelgyro.getAcceleration(&ax, &ay, &az); features[feature_ix++] = (float)ax * CONVERT_G_TO_MS2; features[feature_ix++] = (float)ay * CONVERT_G_TO_MS2; features[feature_ix++] = (float)az * CONVERT_G_TO_MS2; if (feature_ix >= EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) { ei_impulse_result_t result = { 0 }; signal_t signal; numpy::signal_from_buffer(features, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal); EI_IMPULSE_ERROR res = run_classifier(&signal, &result, false); ei_printf("run_classifier returned: %d\n", res); if (res != EI_IMPULSE_OK) return; ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n", result.timing.dsp, result.timing.classification, result.timing.anomaly); for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) { ei_printf("%s:\t%.5f\n", result.classification[ix].label, result.classification[ix].value); if (strcmp(result.classification[ix].label, "drop") == 0 && result.classification[ix].value > 0.8f) { Serial.println("\n🔥 drop detected! Take action!\n"); } } #if EI_CLASSIFIER_HAS_ANOMALY == 1 ei_printf("anomaly:\t%.3f\n", result.anomaly); #endif feature_ix = 0; } } } void ei_printf(const char *format, ...) { static char print_buf[1024] = { 0 }; va_list args; va_start(args, format); int r = vsnprintf(print_buf, sizeof(print_buf), format, args); va_end(args); if (r > 0) { Serial.write(print_buf); } } ``` ![](https://maker.wiznet.io/upload/ckeditor5/244568634%5F1743505023%2Epng) --- Source: https://maker.wiznet.io/Hannah/projects/tinyml-based-object-drop-classifier-using-mpu6050-and-w55rp20-evb-pico/