Task: Motion and Vibration Classification
License: Apache 2.0
This is a prebuilt dataset, collected by Edge Impulse teams, for a gesture recognition system based on continuous motion, for the Continous Motion Recognition tutorial. It contains 15 minutes of data sampled from a MEMS accelerometer at 62.5Hz over the following four classes:
Idle - board sits idly on your desk. There might be some movement detected, e.g. from typing while the board is present.
Snake - board moves over the desk as a snake.
Updown - board moves up and down in a continuous motion.
Wave - board moves left and right like you're waving to someone.
Feature extraction: Spectral Features (FFTs or Wavelets)
Learning block: Classification + optionally Anomaly Detection (K-Means) or Anomaly Detection (GMM)
Not sure what to choose? Try out this dataset with the EON Tuner.
Total Data Items: 101
Labeling Method: single label
Train/Test Split: 84.16% / 15.84%
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.
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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:
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If you use this dataset in your research paper, please cite it using the following BibTeX:
Training Set
Testing Set
Total Data Items
85
16
Labels
idle, snake, updown, wave
anomaly, idle, snake, updown, wave
