Syntiant / Tutorial: Continuous motion recognition - RASynBoard
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
Classify Your Gestures
Have you ever wondered how you can use acceleration to predict the type of input motion? Clone this project to build an embedded ML project to detect various hand gestures from your device's accelerometer!
You can also follow our tutorial to guide you through building your continuous motion recognition model, from data collection to deployment on embedded devices.
Sensor & Block Information
- Accelerometer data from RASynBoard 200Hz 6 axis IMU
- Syntiant IMU Processing Block
- Neural Network Classifier with prediction outputs: "idle (z_openset)", "snake", "updown", "wave"
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Syntiant IMU training data | NPY file | 6904 windows | |
| Syntiant IMU training labels | NPY file | 6904 windows | |
| Classifier model | TensorFlow Lite (float32) | 2 MB | |
| Classifier model | TensorFlow Lite (int8 quantized) | 435 KB | |
| Classifier model | TensorFlow SavedModel | 2 MB | |
| Classifier model | Keras h5 model | 2 MB | |
| Classifier model | Model evaluation metrics (JSON file) | - |
Clone project
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Summary
Data collected
12m 39sProject info
| Project ID | 306343 |
| License | Apache 2.0 |
