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This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant...
A real-time method using only accelerometer data is developed for classifying basic human static postures, namely sitting, standing, and lying, as well as dynamic transitions between them. The algorithm uses discrete wavelet transform (DWT) in combination with a fuzzy logic inference system (FIS). Data from a single three-axis accelerometer integrated into a wearable headband is transmitted wirelessly,...
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