Edge Impulse Inc. / Tutorial: 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
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)
Creating your first impulse (100% complete)
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
Data collected15m 16s
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