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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.
Package in Transit Health (Sensor)
This is the finished Edge Impulse project for the tutorial 'Continuous motion recognition'. From here you can acquire new training data, design impulses and train models.
About this project
Understand if your sensitive packages were not handled with TLC
This project demonstrates how to use the 3-axis accelerometer to classify whether a package had been dropped or violently shaken during its shipment.
You can also follow our tutorial to guide you through building your continuous motion recognition model, from data collection to deployment on embedded devices.
Sensor & Block Information
- Accelerometer data (.cbor files) @ 62.5 Hz
- Spectral Features DSP block for time-based sensor data
- Neural Network Classifier with prediction outputs: "dropped", "idle", "violent_shaking"
- Anomaly Detection block for anomaly score output of unknown motions (motions the model was not trained on)
Run this model
Scan QR code or launch in browser
Dataset summary
Data collected
9m 41sSensors
accX, accY, accZ @ 62.5HzLabels
dropped, idle, violent_shakingProject info
| Project ID | 87707 |
| Project version | 2 |
| License | BSD 3-Clause Clear |
| No. of views | 31,709 |
| No. of clones | 4 |
