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This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
Motion Classification - Continuous motion recognition
This is the finished Edge Impulse project for the tutorial 'Continuous motion recognition'. From here you can acquire new training data, design impulses and train models.
About this project
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.
Compatible Blocks
- 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.
Run this model
On any device
Dataset summary
Data collected
9m 42sSensor
audio @ 44.1KHzLabels
bathroom, help, no, request, yesProject info
| Project ID | 707734 |
| License | 3-Clause BSD |
| No. of views | 228 |
| No. of clones | 0 |
