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 (.cbor files) @ 62.5 Hz
- Spectral Features DSP block for time-based sensor data
- Neural Network Classifier with prediction outputs: "idle", "snake", "updown", "wave"
- Anomaly Detection block for anomaly score output of unknown motions (gestures/motions the model was not trained on)
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Syntiant IMU training data | NPY file | 64 windows | |
| Syntiant IMU training labels | NPY file | 64 windows | |
| Classifier model (version #1) | TensorFlow Lite (float32) | 2 MB | |
| Classifier model (version #1) | TensorFlow Lite (int8 quantized) | 433 KB | |
| Classifier model (version #1) | Model evaluation metrics (JSON file) | 3 KB | |
| Classifier model (version #1) | TensorFlow SavedModel | 2 MB | |
| Classifier model (version #1) | Keras h5 model | 2 MB |
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Summary
Data collected
15m 20sProject info
| Project ID | 306343 |
| License | Apache 2.0 |
