METHODS FOR A MOVEMENT AND VIBRATION ANALYZER (MVA)

摘要

The present patent describes a method for a Movement and Vibration Analyzer (MVA) based on Fast Fourier Transform spectral analysis, and empirical mode decomposition (EMD) for Hilbert transform of a timeseries recorded with an accelerometer attached to a human being or an object. The medical application is the detection of Parkinson's disease (PD) and other neurological motor disorders (Dystonias, Dyskinesias, Huntington's disease, Essential Tremor, Multiple System Atrophy (MSA), etc), which affects worldwide more than 5 million persons, where the highest percentage is in the ageing population. The industrial application is the study of vibration and maintenance of rotational devices (motors, turbines, and others which have an intrinsic sinusoidal likewise movement). An EMD is carried out on the acceleration signal which produces a collection of intrinsic mode functions (IMF), on which the Hilbert transform is carried out. A set of parameters extracted from the Hilbert Transformed signal gives information of the deviation of the discontinuities. (1) Number of peaks of the derivative of the Hilbert phase higher than a threshold and normalized to time length of the signal and sampling frequency. (2) Variance or standard deviation of the derivative of the Hubert phase, phi'H(t). (3) Fractal dimension (DF) of the curve (HR(t), HI(t)), Hilbert plane. From the power spectrum estimate of the acceleration signal, the parameters used are: (4) Mean frequency. (5) Frequencies of the N main components. These five parameters are combined using fuzzy logic or an ordinal multiple logistic regression to define the movement index (MI), an index from 0 to 100, where 0 indicates no deviation from the sinusoidal movement while increasing numbers indicate larger deviation from the sinusoidal movement.