HILBERT TRANSFORM AND GAIT ANALYSIS: A NEW METHOD TO EVALUATE GAIT VARIABILITY

2018 ◽  
Author(s):  
G. S. Souza ◽  
A. O. Andrade ◽  
M. F. Vieira
Keyword(s):  
Gait Analysis ◽  
Hilbert Transform ◽  
Gait Variability ◽  
New Method

Gait variability analysis through phase portrait estimated from the Hilbert transform

2018 ◽  
Vol 21 (11) ◽  
pp. 645-653
Author(s):  
Gustavo Souto de Sá e Souza ◽  
Adriano O. Andrade ◽  
Marcus Fraga Vieira
Keyword(s):  
Phase Portrait ◽  
Hilbert Transform ◽  
Gait Variability ◽  
Variability Analysis ◽  
The Hilbert Transform

Wavefield decomposition in arbitrary direction and an imaging condition based on stratigraphic dip

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. S299-S312
Author(s):  
Xuebao Guo ◽  
Ying Shi ◽  
Weihong Wang ◽  
Hongliang Jing ◽  
Zhen Zhang
Keyword(s):  
Hilbert Transform ◽  
New Method ◽  
Frequency Noise ◽  
Arbitrary Direction ◽  
Vector Method ◽  
Imaging Condition ◽  
Dip Angle ◽  
The Hilbert Transform ◽  
Angle Gather ◽  
Wavefield Decomposition

In reverse time migration (RTM), wavefield decomposition can play an important role in addressing the issue of migration noise, especially low-frequency noise. The complete wavefield decomposition based on the Hilbert transform is a commonly used method in RTM, but it is accompanied by extra wavefield simulation and wavefield storage. We have developed three distinct methods. The first is a convenient method for wavefield decomposition, which is based on Poynting vectors. Only the unit vector in one direction is needed to realize the wavefield decomposition in an arbitrary direction by this method. It breaks through the limitation that the Hilbert transform-based method is applicable only to the up- and downgoing wave or left- and right-going wave decomposition, and the calculation cost is negligible compared with RTM. The second is a method based on the instantaneous wavenumber, which we developed for calculating the wave propagation direction. On the basis of wavefield decomposition, the imaging angle gather from the new method performs better than that of the Poynting vector method. Meanwhile, it also is used for generating the incident angle gather and dip angle gather. The latter expresses the dip angle of underground strata. More importantly, the above methods allow us to control the wavefield decomposition direction and three angles at any position underground. The third adopts a stratigraphic imaging condition method, and we briefly analyze the relationship between the new method and the inverse-scattering imaging condition. The stratigraphic imaging condition maps the results to the dip angle of the stratum through a spatial gradient wavefield, which can enhance the effective imaging information. The above three kinds of angle gathers also can be constructed by the stratigraphic imaging condition. Numerical experiments demonstrate that the imaging results and the angle gathers obtained by our proposed method have higher accuracy and resolution.

scholarly journals Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sungmoon Jeong ◽  
Hosang Yu ◽  
Jaechan Park ◽  
Kyunghun Kang
Keyword(s):  
Deep Learning ◽  
Gait Analysis ◽  
Normal Pressure Hydrocephalus ◽  
Learning Algorithms ◽  
Normal Pressure ◽  
Gait Variability ◽  
Stride Time ◽  
Analysis Method ◽  
Gait Parameters ◽  
Analysis System

AbstractA vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system. There was a strong correlation in 11 gait parameters between our vision-based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision-based gait analysis system were correlated with FAB scores. Vision-based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision-based gait analysis for INPH patients.

A new method for calculating iron loss based on Hilbert Transform

2010 ◽  
Author(s):  
Junquan Chen ◽  
Weiming Ma ◽  
Dong Wang ◽  
Fuhua Li ◽  
Yunjun Guo
Keyword(s):  
Hilbert Transform ◽  
New Method ◽  
Iron Loss

scholarly journals FRAILTY ASSESSMENT BASED ON THE QUALITY OF DAILY WALKING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S85-S85 ◽  
Author(s):  
Danya Pradeep Kumar ◽  
Nima Toosizadeh ◽  
Jane Mohler ◽  
Kaveh Laksari
Keyword(s):  
Physical Activity ◽  
Gait Analysis ◽  
Logistic Models ◽  
Gait Variability ◽  
Geriatric Syndrome ◽  
Age Related ◽  
Power Spectral ◽  
Gait Parameters ◽  
Clinical Gait Analysis

Abstract Frailty is an increasingly recognized geriatric syndrome resulting in age-related decline in reserve across multiple physiologic systems. An impaired physical function is a prime indicator of frailty. In this study, we aim to implement a body-worn sensor to characterize the quantity and quality of everyday walking, and establish associations between gait impairment and frailty. Daily physical activity was acquired for 48 hours from 125 older adults (≥65 years; 44 non-frail, 60 pre-frail, and 21 frail based on the Fried gold standard) using a tri-axial accelerometer motion-sensor. Continuous purposeful walks (≥60s) without pauses were identified from time-domain acceleration data. Power spectral density (PSD) analysis was performed to define higher gait variability, which was identified by a shorter and wider PSD peak. Association between frailty and gait parameters was assessed using multivariable nominal logistic models with frailty as the dependent variable, and demographic parameters along with the gait parameters as the independent variables. Stride times, PSD gait variability, and total and maximum continuous purposeful walking duration were significantly different between non-frail and pre-frail/frail groups (p<0.05). Using a step-wise model with the above qualitative and quantitative gait parameters as predictors, the pre-frail/frail group (vs. non-frail) was identified with 71.4% sensitivity and 75.4% specificity. Everyday walking characteristics were found to be accurate determinants of frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the stages of frailty. In-home gait analysis is advantageous over clinical gait analysis as it enables cost- and space-effective continuous monitoring.

A new method for measuring AFO deformation, tibial and footwear movement in three dimensional gait analysis

2013 ◽  
Vol 38 (4) ◽  
pp. 1074-1076 ◽  
Author(s):  
Emily Ridgewell ◽  
Morgan Sangeux ◽  
Timothy Bach ◽  
Richard Baker
Keyword(s):  
Gait Analysis ◽  
Three Dimensional ◽  
New Method
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