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This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
Crane_Monitoring_with_Arduino_Nano_33_BLE_Sense
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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Dataset summary
Data collected
4m 50sSensors
accX, accY, accZ @ 62.5HzLabels
motion, stop, vibrationsProject info
| Project ID | 53271 |
| Project version | 8 |
| License | No license attached |
| No. of views | 12,885 |
| No. of clones | 8 |
