Wavelet theory is a relatively new branch of applied mathematics which has wide applicability in many scientific and engineering areas, especially in signal and image processing. This theory is useful for analyzing signals at different scales. Signals can be viewed at different levels to determine general trends as well as subtle changes or transient, short duration high frequency components. Wavelet transforms provide a powerful complement to traditional Fourier-based methods.; Wavelet theory has enjoyed great success in biomedical signal processing. However, very little work has been done in applying this theory to biomechanical signals. This research applies wavelet noise removal techniques to biomechanical data. These signals are often characterized by sharp transient components. Traditional techniques remove the noise, but attenuate or altogether erase sharp features. A graphically-based software system has been developed to implement wavelet-based noise removal techniques to biomechanical signals.
展开▼
