Continuous motion recognition
Last updated
Last updated
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%
Training Set | Testing Set | |
Total Data Items | 85 | 16 |
Labels | idle, snake, updown, wave | anomaly, idle, snake, updown, wave |
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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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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.
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