RESUMEN
Objective To solve the problem in motion signal extraction due to nonstationarity.Methods The calf angular acceleration signal was taken as an example,and the time-frequency analysis was carried out by using Hilbert-Huang transform (HHT).Then,the empirical mode decomposition (EMD) of the signal was executed to obtain some intrinsic mode functions (IMFs),and then all or some intrinsic mode functions were subjected to a Hilbert transform to obtain Hilbert spectrum of the signal.Results In the mode of stomping,walking and falling,when the test-feet touched the ground,the energy was concentrated at the frequencies of 4 Hz,3 Hz and 2 Hz,and the time-frequency resolution was higher,and the information of these behaviors was mainly focused on the high-frequency components of the intrinsic mode functions.Conclusion The time-frequency characteristics of the human body calf angular acceleration signal can be applied to other kinematic parameters of the lower limbs in order to obtain the movement characteristics of the lower limbs.
RESUMEN
Objective To propose a method for analyzing the hip joint signals during human walking based on Hilbert-Huang transform (HHT) method and verify its feasibility. Methods First, the hip joint angles of one healthy subject were measured by using the hip joint measuring platform composed of acceleration sensors and gyroscopes. Second, all intrinsic mode functions (IMFs) at different scales, which could be further analyzed and combined, were obtained by applying the ensemble empirical mode decomposition (EEMD) to original signals. Finally, the Hilbert spectrum of original signals were plotted and analyzed. Results The signals representing different motion modes as well as gait characteristics indicated by rotating track of the hip joint were obtained. The Hilbert spectrum could show the intra-wave frequency modulation in the main motion mode and the characteristics of walking frequencies. Conclusions This method can be used in rehabilitation and treatment of patients with gait diseases. By using this method, the characteristic signals of the hip joints at different frequency scales can be effectively decomposed, and the post-processing signals can be filtered and centrally corrected, so as to adaptively analyze gait signals of the patients.