Kutluhan Aktar / BLE Smartwatch Detecting Potential Sun Damage Public

Kutluhan Aktar / BLE Smartwatch Detecting Potential Sun Damage

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

About this project

Project Description

I built this artificial neural network model (ANN) for my BLE AI-driven smartwatch project. The model makes predictions on sun damage risk levels (classes) based on UV index, temperature, pressure, and altitude measurements:

  • Tolerable
  • Risky
  • Perilous

After building the neural network model, I deployed the model as an Arduino library and uploaded it to XIAO BLE. Also, I employed XIAO BLE to transmit (advertise) the prediction (detection) result and the recently collected data over BLE.

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
Raw data training data NPY file 135 windows
Raw data training labels NPY file 135 windows
Raw data testing data NPY file 22 windows
Raw data testing labels NPY file 22 windows
NN Classifier model TensorFlow Lite (float32) 34 KB
NN Classifier model TensorFlow Lite (int8 quantized) 10 KB
NN Classifier model TensorFlow SavedModel 37 KB
NN Classifier model Keras h5 model 32 KB

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Summary

Data collected
2m 37s

Project info

Project ID 107355
Project version 1
License No license attached