Task: Sensor Fusion Classification
License: Apache 2.0
This dataset has been collected by Edge Impulse team to demonstrate how to perform Sensor Fusion Classification leveraging Neural Networks Embeddings.
Sensor fusion refers to the process of combining data from different types of sensors to give more information to the neural network. To extract meaningful information from this data, you can use the same DSP block, multiples DSP blocks, or use neural networks embeddings.
You can have a look at this tutorial for more information to see how to use this dataset.
This dataset combines audio data and accelerometer data.
Feature extraction: Audio Embeddings (Spectrogram + Convolution Neural Network Embeddings) + Spectral Analysis (accelerometer)
Learning block: Classification using a fully-connected network
Total Data Items: 36
Labeling Method: single label
Train/Test Split: 91.67% / 8.33%
Clone the public project.
To clone and use this project, visit the Edge Impulse Studio link, click on the Clone button on the top-right corner and follow the cloning instructions.
Download
HuggingFace - Soon
Kaggle - Soon
Import this dataset to your Edge Impulse project
This project uses the Edge Impulse Exporter Format (info.labels). See this documentation page for more info.
Edge Impulse also supports different data sample formats and dataset annotation formats that you can import into your project to build your edge AI models:
Upload portals (Enterprise feature)
If you use this dataset in your research paper, please cite it using the following BibTeX:
Training Set
Testing Set
Total Data Items
33
3
Labels
extract, grind, idle, pump
