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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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 50sProject info
Project ID | 53271 |
Project version | 8 |
License | No license attached |