Dhruv Sheth / Environmental-sensing-Anomaly-detection Public

Dhruv Sheth / Environmental-sensing-Anomaly-detection

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

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
Flatten training data NPY file 122 windows
Flatten training labels NPY file 122 windows
Flatten testing data NPY file 72 windows
Flatten testing labels NPY file 72 windows
Spectral features training data NPY file 122 windows
Spectral features training labels NPY file 122 windows
Spectral features testing data NPY file 72 windows
Spectral features testing labels NPY file 72 windows
Anomaly detection model JSON 21 KB

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Summary

Data collected
4m 59s

Project info

Project ID 17607
Project version 1