Motion feature extraction
Last updated
Last updated
Motion feature extraction is a key component in many applications, including activity recognition, gesture control, and vibration analysis. In this concept article, we'll explore the basics of motion feature extraction, its importance, and how to implement it using Edge Impulse, specifically for Edge AI use cases. At Edge Impulse, when speaking about feature extraction techniques, we also use the terms DSP (Digital Signal Processing) or pre-processing.
Motion feature extraction involves transforming raw motion sensor data (such as accelerometer or gyroscope readings) into a set of meaningful features that can be used for further processing or analysis. These features capture essential characteristics of the motion signal, such as its frequency content, amplitude, and temporal dynamics.
Raw motion data is often too complex and voluminous to be directly used for machine learning tasks. Feature extraction simplifies the motion signal, making it easier to analyze and interpret. This process helps in reducing the dimensionality of the data while retaining the most informative aspects, improving the performance of machine learning models, especially in Edge AI applications where computational resources are limited.
Edge Impulse offers a powerful Spectral Features block to extract key motion features, simplifying the development process for Edge AI applications. This block supports two main types of analysis:
Fast Fourier Transform (FFT): Transforms the time-domain signal into the frequency domain, providing information about the signal's frequency content. It is best suited for analyzing repetitive patterns in a signal.
Wavelet Transform: Decomposes the signal into components at various scales, capturing both frequency and temporal information. It works better for complex signals that have transients or irregular waveforms.
Note that you can also import your own feature extraction block so you can use it directly in Edge Impulse Studio. See Custom processing blocks.
