Alejandro Celis / Crane_Monitoring_with_Arduino_Nano_33_BLE_Sense Public

Alejandro Celis / Crane_Monitoring_with_Arduino_Nano_33_BLE_Sense

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

Crane Monitoring with Arduino Nano 33 BLE Sense

This project was done as part of the Coursera course Introduction to Embedded Machine Learning by Shawn Hymel and Alexander Fred-Ojala.

The objective of the project is to demonstrate how machine learning in embedded devices can be used to monitor motion and vibration in machines with the help of Edge Impulse. I picked a toy tower crane as the machine to be monitored and an Arduino Nano 33 BLE Sense as the development board (it comes with an in-built 3-axis accelerometer sensor which we will be using). The steps followed in this project are similar in general to those described in the Continous Motion Recognition tutorial.

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Creating your first impulse (100% complete)

Acquire data

Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.

Design an impulse

Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.

Deploy

Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.

Download block output

Title Type Size
Spectral features training data NPY file 1484 windows
Spectral features training labels NPY file 1484 windows
Spectral features testing data NPY file 552 windows
Spectral features testing labels NPY file 552 windows
NN Classifier model TensorFlow Lite (float32) 6 KB
NN Classifier model TensorFlow Lite (int8 quantized) 4 KB
NN Classifier model TensorFlow SavedModel 16 KB

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Summary

Data collected
4m 50s

Project info

Project ID 70728
Project version 8