scholarly journals Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3462 ◽  
Author(s):  
Ning Ji ◽  
Hui Zhou ◽  
Kaifeng Guo ◽  
Oluwarotimi Samuel ◽  
Zhen Huang ◽  
...  
Keyword(s):  
Event Detection ◽  
Selection Criteria ◽  
Tibialis Anterior Muscle ◽  
Heel Strike ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Wavelet Selection ◽  
Effective Assessment ◽  
Mother Wavelets ◽  
Selection Of

Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.

scholarly journals Relationship Between Knee Extensors Power Output and Vastus Lateralis EMG Activation in Elderly Women: Influence of Mother Wavelet Selection

2017 ◽  
Vol 10 (1) ◽  
pp. 263-271
Author(s):  
João Pedro Pinho ◽  
Bruno Mezêncio ◽  
Desidério Cano Porras ◽  
Julio Cerca Serrão ◽  
Alberto Carlos Amadio
Keyword(s):  
Cross Correlation ◽  
Elderly Women ◽  
Vastus Lateralis ◽  
Biceps Femoris ◽  
Knee Extension ◽  
Knee Extensors ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Wavelet Selection ◽  
Mother Wavelets

Purpose:The main objective of this study was to compare frequency parameters produced by six mother wavelets pinpointing the most feasible to investigate electromyographic (EMG) parameters while producing knee extension power in elderly women. The influence of different load conditions in mother wavelet selection and power output were also analyzed.Methods:Thirteen sedentary elderly women (69.3 ± 4.1 years) took part in the study. Participants executed 6 repetitions of 3 load condition (30%, 50% and 70% of the maximal) with the concentric phase of the knee extension movement as quickly as possible. Kinematic data obtained by video analysis, an anthropometric model and Newtonian mechanics were used to calculate knee extensors’ power. A continuous wavelet analysis was used as a time-frequency transformation strategy of vastus lateralis and biceps femoris EMG data and six different mother wavelets were selected: Morlet; 4th, 8th and 44th order Daubechie, 4th order Coiflet and 5th order Symlet.Results:44th order Daubechie showed the highest maximal cross correlation value and no differences were seen between different mother wavelets and cross correlation at zero lag and in the lag variable. Although increased knee extensors peak power at higher loads were seen, no differences in vastus lateralis or biceps femoris root mean square values were obtained.Conclusion:44th order Daubechie mother wavelet was pinpointed as the most suitable to obtain EMG time-frequency parameters. We have also seen that different load conditions do not seem to have an influence on mother wavelet selection.

scholarly journals Mother wavelet selection using signal energy for cracks detection in the rotation shafts

2020 ◽  
Vol XXIII (2) ◽  
pp. 64-74
Author(s):  
Pricop Codruta
Keyword(s):  
Selection Criteria ◽  
Selection Criterion ◽  
Mother Wavelet ◽  
Signal Energy ◽  
Frequency Bands ◽  
Optimum Selection ◽  
Rotating Shafts ◽  
Wavelet Selection ◽  
Energy Dissipated ◽  
Selection Of

The mother wavelet greatly influences the wavelet analysis of a non-stationary and nonlinear recorded signal. Choosing mother wavelet must be done to determine cracks in rotating shafts so as to take into account the nature and type of information signals to be extracted from the signal. The difficulty in optimum selection of the mother wavelet is determined by their complex properties that determine different selection criteria. In the paper, several families of functions (Haar, Daubechies, Symlets, Coiflet, BiorSplines) were used for analysis and the proposed selection criterion is the energy dissipated on the frequency bands. Signal recordings were made on a stand to determine the presence of cracks in rotating shafts and their classification. For discrete decomposition of recorded signals (DWT) and the calculation of energy dissipated on the frequency bands the Matlab wavelet instrument was used.

Comparing mother wavelet selection criteria for road pavements NDT monitoring

2019 ◽  
pp. 618-623 ◽  
Author(s):  
R. Fedele ◽  
F.G. Praticò ◽  
R. Carotenuto ◽  
F.G. Della Corte
Keyword(s):  
Selection Criteria ◽  
Mother Wavelet ◽  
Wavelet Selection

scholarly journals Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

2019 ◽  
Vol 9 (7) ◽  
pp. 1345 ◽  
Author(s):  
Manel Rhif ◽  
Ali Ben Abbes ◽  
Imed Farah ◽  
Beatriz Martínez ◽  
Yanfang Sang
Keyword(s):  
Applied Sciences ◽  
Time Series ◽  
Wavelet Transform ◽  
Learning Algorithm ◽  
Decomposition Methods ◽  
Stationary Time Series ◽  
Time Frequency ◽  
Stationary Time ◽  
Wavelet Selection ◽  
Selection Of

Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.

Wavelet Analysis: Mother Wavelet Selection Methods

2013 ◽  
Vol 393 ◽  
pp. 953-958 ◽  
Author(s):  
Wai Keng Ngui ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Wavelet Analysis ◽  
Frequency Analysis ◽  
Singularity Analysis ◽  
Selection Methods ◽  
Mother Wavelet ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Main Challenge ◽  
Wide Range ◽  
Wavelet Selection

Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

A NEW STRUCTURE AND TRAINING PROCEDURE FOR MULTI-MOTHER WAVELET NETWORKS

2010 ◽  
Vol 08 (01) ◽  
pp. 149-175 ◽  
Author(s):  
MOHAMED OTHMANI ◽  
WAJDI BELLIL ◽  
CHOKRI BEN AMAR ◽  
ADEL M. ALIMI
Keyword(s):  
Function Approximation ◽  
Least Squares Method ◽  
Two Dimensions ◽  
Training Procedure ◽  
Wavelet Network ◽  
Mother Wavelet ◽  
Wavelet Networks ◽  
And Training ◽  
Mother Wavelets ◽  
Selection Of

This paper deals with the features of a new wavelet network structure founded on several mother wavelets families. This new structure is similar to the classic wavelets network but it admits some differences eventually. The wavelet network basically uses the dilations and translations versions of only one mother wavelet to construct the network, but the new one uses several mother wavelets and the objective is to maximize the probability of selection of the best wavelets. Two methods are presented to assist the training procedure of this new structure. On one hand, we have an optimal selection technique that is based on an improved version of the Orthogonal Least Squares method; on the other, the Generalized Cross-Validation method to determine the number of wavelets to be selected for every mother wavelet. Some simulation results are reported to demonstrate the performance and the effectiveness of the new structure and the training procedure for function approximation in one and two dimensions.

Selection of one-dimensional sedimentation: models for on-line use

1995 ◽  
Vol 31 (2) ◽  
pp. 193-204 ◽  
Author(s):  
Koen Grijspeerdt ◽  
Peter Vanrolleghem ◽  
Willy Verstraete
Keyword(s):  
Steady State ◽  
Selection Criteria ◽  
Data Sets ◽  
Concentration Profiles ◽  
A Posteriori ◽  
One Dimensional ◽  
On Line ◽  
Dynamic Concentration ◽  
Selection Of ◽  
Modelling Task

A comparative study of several recently proposed one-dimensional sedimentation models has been made. This has been achieved by fitting these models to steady-state and dynamic concentration profiles obtained in a down-scaled secondary decanter. The models were evaluated with several a posteriori model selection criteria. Since the purpose of the modelling task is to do on-line simulations, the calculation time was used as one of the selection criteria. Finally, the practical identifiability of the models for the available data sets was also investigated. It could be concluded that the model of Takács et al. (1991) gave the most reliable results.

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