Assessment of Waveform Similarity in Clinical Gait Data: The Linear Fit Method

The assessment of waveform similarity is a crucial issue in gait analysis for the comparison of kinematic or kinetic patterns with reference data. A typical scenario is in fact the comparison of a patient’s gait pattern with a relevant physiological pattern. This study aims to propose and validate a simple method for the assessment of waveform similarity in terms of shape, amplitude, and offset. The method relies on the interpretation of these three parameters, obtained through a linear fit applied to the two data sets under comparison plotted one against the other after time normalization. The validity of this linear fit method was tested in terms of appropriateness (comparing real gait data of 34 patients with cerebrovascular accident with those of 15 healthy subjects), reliability, sensitivity, and specificity (applying a cluster analysis on the real data). Results showed for this method good appropriateness, 94.1% of sensitivity, 93.3% of specificity, and good reliability. The LFM resulted in a simple method suitable for analysing the waveform similarity in clinical gait analysis.

Download Full-text

Assisted traveltime picking of crosshole GPR data

Geophysics ◽

10.1190/1.3141002 ◽

2009 ◽

Vol 74 (4) ◽

pp. J35-J48 ◽

Cited By ~ 9

Author(s):

Bernard Giroux ◽

Abderrezak Bouchedda ◽

Michel Chouteau

Keyword(s):

Time Window ◽

Signal To Noise Ratio ◽

Real Data ◽

Information Criterion ◽

High Signal ◽

Data Sets ◽

Signal To Noise ◽

Arrival Times ◽

Common Time ◽

Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

Download Full-text

Simple method to reduce the effect of patient positioning variation on three-dimensional motion analysis during treadmill gait

Clinics and Practice ◽

10.4081/cp.2013.e30 ◽

2013 ◽

Vol 3 (2) ◽

pp. 30

Author(s):

Shigeo Tanabe ◽

Eiichi Saitoh ◽

Kei Ohtsuka ◽

Toshio Teranishi ◽

Yutaka Tomita ◽

...

Keyword(s):

Gait Analysis ◽

Time Series Data ◽

Three Dimensional ◽

Patient Positioning ◽

Gait Pattern ◽

Closed Curve ◽

Series Data ◽

Simple Method ◽

Pass Filter ◽

Anterior Posterior

Recently, three-dimensional (3D) closed curve trajectories of markers placed at strategic body locations, called <em>cyclograms</em> or <em>Lissajous-like graphs</em>, are used for treadmill gait analysis. A simple method is presented to reduce the effect of patient positioning variation. After breaking down movement into three components (anterior-posterior, medial-lateral and superior-inferior), the time-series data and time-inverted data are serially concatenated. A fast Fourier transform (FFT) is done, and a high-pass filter (except 0 Hz) is applied to the anterior-posterior and medial-lateral components. Next an inverse FFT is executed, and the posterior half of the outcome, corresponding to time-inverted data, is deleted. The 3D closed curve is then reconstructed. Results showed that the proposed method was able to reduce the effect of patient positioning variation. Since the adjusted curve is simply a symbolized gait pattern, the method might be useful as an adjunct tool in observational gait analysis.

Download Full-text

A Simple Method to Obtain Consistent and Clinically Meaningful Pelvic Angles from Euler Angles during Gait Analysis

Journal of Applied Biomechanics ◽

10.1123/jab.23.3.218 ◽

2007 ◽

Vol 23 (3) ◽

pp. 218-223 ◽

Cited By ~ 3

Author(s):

Tishya A.L. Wren ◽

Paul C. Mitiguy

Keyword(s):

Gait Analysis ◽

Elevation Angle ◽

Euler Angle ◽

Technical Note ◽

Euler Angles ◽

Simple Method ◽

Lean Angle ◽

Angle Rotation ◽

Clinical Gait Analysis ◽

Rotation Sequence

Clinical gait analysis usually describes joint kinematics using Euler angles, which depend on the sequence of rotation. Studies have shown that pelvic obliquity angles from the traditional tilt-obliquity-rotation (TOR) Euler angle sequence can deviate considerably from clinical expectations and have suggested that a rotation-obliquity-tilt (ROT) Euler angle sequence be used instead. We propose a simple alternate approach in which clinical joint angles are defined and exactly calculated in terms of Euler angles from any rotation sequence. Equations were derived to calculate clinical pelvic elevation, progression, and lean angles from TOR and ROT Euler angles. For the ROT Euler angles, obliquity was exactly the same as the clinical elevation angle, rotation was similar to the clinical progression angle, and tilt was similar to the clinical lean angle. Greater differences were observed for TOR. These results support previous findings that ROT is preferable to TOR for calculating pelvic Euler angles for clinical interpretation. However, we suggest that exact clinical angles can and should be obtained through a few extra calculations as demonstrated in this technical note.

Download Full-text

Transforming variables to central normality

Machine Learning ◽

10.1007/s10994-021-05960-5 ◽

2021 ◽

Author(s):

Jakob Raymaekers ◽

Peter J. Rousseeuw

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimator ◽

Simulation Study ◽

Real Data ◽

Data Sets ◽

Transformation Parameter ◽

Likelihood Estimator ◽

Extensive Simulation ◽

Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

Download Full-text

A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽

10.3390/e23010062 ◽

2020 ◽

Vol 23 (1) ◽

pp. 62

Author(s):

Zhengwei Liu ◽

Fukang Zhu

Keyword(s):

Likelihood Estimation ◽

Real Data ◽

Autoregressive Models ◽

Superior Performance ◽

Data Sets ◽

Binomial Thinning ◽

Free Case ◽

Two Parameters ◽

Conditional Maximum ◽

Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

Download Full-text

Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽

10.3390/econometrics9010010 ◽

2021 ◽

Vol 9 (1) ◽

pp. 10

Author(s):

Šárka Hudecová ◽

Marie Hušková ◽

Simos G. Meintanis

Keyword(s):

Goodness Of Fit ◽

Probability Generating Function ◽

Parametric Bootstrap ◽

Real Data ◽

Data Sets ◽

Test Statistics ◽

Finite Sample ◽

Generalized Poisson ◽

Goodness Of Fit Tests ◽

Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

Download Full-text

TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

Information ◽

10.3390/info12050202 ◽

2021 ◽

Vol 12 (5) ◽

pp. 202

Author(s):

Louai Alarabi ◽

Saleh Basalamah ◽

Abdeltawab Hendawi ◽

Mohammed Abdalla

Keyword(s):

Infectious Diseases ◽

Infected Patient ◽

Public Health Problem ◽

Real Data ◽

Exposure Period ◽

Contact Tracing ◽

Data Sets ◽

Major Public Health Problem ◽

Real Time Processing ◽

Recent Developments

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.

Download Full-text

The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Symmetry ◽

10.3390/sym13030474 ◽

2021 ◽

Vol 13 (3) ◽

pp. 474

Author(s):

Abdulhakim A. Al-Babtain ◽

Ibrahim Elbatal ◽

Hazem Al-Mofleh ◽

Ahmed M. Gemeay ◽

Ahmed Z. Afify ◽

...

Keyword(s):

Numerical Simulations ◽

Exponential Distribution ◽

Real Data ◽

Exponential Model ◽

Statistical Properties ◽

Engineering Science ◽

Data Sets ◽

Engineering Sciences ◽

General Statistical ◽

Anderson Darling

In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

Download Full-text