Window size in (acceleration) signal segmentation for activity classification is important because it needs to capture necessary characteristics of the signals. Various sizes of window have been used in previous works without stating a specific reason. A window size is selected based on past experiments and hardware limitations. Majority of approaches used window size in […]
Tag: window size
Features Extraction
Ravi et al. extracted features from raw data using a window size of 256 samples with 128 overlapping between consecutive windows. Sampling rate used is 50Hz which equivalent to 5.12s for each window. The calculated features are mean, standard deviation, energy and correlation. A single triaxial accelerometer was worn near the pelvic region in the […]