Kinematic Gait Patterns in Competitive and Recreational Runners

2017 ◽  
Vol 33 (4) ◽  
pp. 268-276 ◽  
Author(s):  
Christian A. Clermont ◽  
Sean T. Osis ◽  
Angkoon Phinyomark ◽  
Reed Ferber
Keyword(s):  
Classification Accuracy ◽  
Three Dimensional ◽  
Principal Component ◽  
Treadmill Running ◽  
Support Vector ◽  
Gait Kinematics ◽  
Ankle Eversion ◽  
Machine Learning Approach ◽  
Recreational Runners ◽  
Time Point

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the ‘competitive’ (n = 20) or ‘recreational’ group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, ‘typical’ running patterns that may help explain differences in injury mechanisms.

scholarly journals Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1321
Author(s):  
Wenjing Quan ◽  
Huiyu Zhou ◽  
Datao Xu ◽  
Shudong Li ◽  
Julien S. Baker ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Sagittal Plane ◽  
Three Dimensional ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Ankle Inversion ◽  
Motion System ◽  
Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

Image Texture Based Hybrid Diagnostic Tool for Kidney Disease Classification

2018 ◽  
Vol 8 (9) ◽  
pp. 1899-1908
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Diagnostic Tool ◽  
Classification Accuracy ◽  
Wavelet Coefficient ◽  
Region Of Interest ◽  
Principal Component ◽  
Disease Classification ◽  
Image Texture ◽  
Support Vector ◽  
Adaptive Wavelet ◽  
Medical Diagnostic

The identification of chronic medical conditions and its associated mortality has led to the emergence of less invasive methods for medical diagnostic imaging. This work proposes a Computer Aided Diagnostic tool useful in automatic classification of kidney images as normal, simple cysts, kidney stones and the less investigated complex cystic renal cell carcinoma. The first part of the work investigates an effective despeckling algorithm with a proposed adaptive wavelet based denoising technique. Encouraging increased PSNR values ranging from 15 dB to 24 dB were obtained. Second part of work suggests a set of wavelet coefficient based feature set which showed a classification accuracy of 92.2%, better by 20.3% to 0.8% against existing methods. The final part of the work to develop a complete tool for kidney image classification combines the proposed wavelet based features with three existing statistical based feature sets yielded a classification accuracy of 96.9%. The suggested features were extracted from the region of interest from an image set. A reduced feature set of 18 from the original size of 163 was obtained using principal component analysis and applied for training a support vector machine classifier.

Identification of the Seaweed Fluorescence Spectroscopy Based on the PCA and ICA-SVM

2012 ◽  
Vol 182-183 ◽  
pp. 1958-1961
Author(s):  
Jiang Tao Lv ◽  
Qiong Chan Gu
Keyword(s):  
Fluorescence Spectrum ◽  
Three Dimensional ◽  
Characteristic Root ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Component Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Fluorescent Light ◽  
Fast Method

Current years, the offing red tide of china is recurrent mutation, the direct and fast method which can analyze the amount and the kind of the phytoplankton is needed imperious. Three-dimensional fluorescence spectrum can show entire fingerprint information of fluorescent light that in the range of excitation and emission wavelength, but the dimension of three-dimensional fluorescence spectrum is higher, the characteristic spectrum of different kinds pelagic plant are multifarious, it is complex identification. In this paper, the principal component analysis (PCA) is used to reduce the dimensions of the spectroscopy. The independent component analysis (ICA) is used to do the matrix decomposition from the perspective of independence to extract the main feature of the spectroscopy data processed by the PCA. The support vector machine (SVM) is used to assort the main characteristic root books which are abstracted by the ICA. The correct laboratory sorting of seaweed is realized. Experimental result indicate, this method can identify the chief component of admixture seaweed, the high dimensional spectroscopy information of seaweed is proceed effective feature extraction, the sorting speed is increase greatly, the discrimination of sorting is reach 92% percent.

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

scholarly journals CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 411-416
Author(s):  
M. Gu ◽  
S. Lyu ◽  
M. Hou ◽  
S. Ma ◽  
Z. Gao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Hyperspectral Image ◽  
Research Result ◽  
Principal Component ◽  
Shanxi Province ◽  
Support Vector ◽  
Archaeological Research ◽  
Naked Eye ◽  
Archaeological Work

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

scholarly journals A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

2021 ◽  
Vol 13 (9) ◽  
pp. 239
Author(s):  
Danveer Rajpal ◽  
Akhil Ranjan Garg ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano
Keyword(s):  
Character Recognition ◽  
Large Population ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Hybrid Features ◽  
Efficient Management ◽  
Novel Approach ◽  
Proposed Model ◽  
Machine Learning Approach

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

scholarly journals Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Wang ◽  
Xi Liu ◽  
Chongchong Yu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Support Vector ◽  
Image Size ◽  
Resonance Imaging

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

scholarly journals Automatic Subtyping of Individuals with Primary Progressive Aphasia

2021 ◽  
pp. 1-10
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

Background: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Objective: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. Methods: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians’ classifications. Results: The DNN model outperformed the other machine learning models as well as expert clinicians’ classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. Conclusion: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

scholarly journals COMPUTATIONAL APPROACHES FOR THE PREDICTION OF ANTIMICROBIAL POTENTIAL PEPTIDES FROM OCIMUM TENUIFLORUM.

2018 ◽  
Vol 11 (1) ◽  
pp. 398
Author(s):  
Sunil Kumar Suryawanshi ◽  
Usha Chouhan
Keyword(s):  
Three Dimensional ◽  
Docking Studies ◽  
Dimensional Structure ◽  
Support Vector ◽  
Learning Approach ◽  
Discovery Studio ◽  
Peptide Docking ◽  
Machine Learning Approach ◽  
Ocimum Tenuiflorum ◽  
Novel Target

 Objective: In this study, antimicrobial activity was predicted against novel antimicrobial target 1SDE receptor to understand the structural feature of predicted peptides using machine learning approach from Ocimum tenuiflorum. Methods: Protein sequences from O. tenuiflorum were digested using peptide cutter and further processed for the prediction of antimicrobial peptide (AMP) through AMP predictor tool of CAMP which have multidimensional algorithms such as support vector machine, artificial neural network, random forest, and discriminant analysis. After analyzing various peptides, only four peptides were predicted as antimicrobial in nature. Furthermore, the three-dimensional structure of different potential peptides was generated with the help of Pepfold-3.0 server followed by protein-peptide docking studies with novel target receptor with the help of PatchDock, FireDock webserver, and Hex 8.0 software. Interactions were further visualized using Discovery Studio Client 2.5 software tool.Results: This study revealed that peptide 2 resulted higher score in PatchDock and FireDock and also Hex 8.0 provides E total value of −430.18 which is higher than that of peptide 1 with −381.07, peptide 3 with −416.86, and peptide 4 with −407.94.Conclusion: The peptide predicted in this study has potential to act as effective AMP against target receptor and also utilize other antimicrobial target.

scholarly journals Differential Privacy Principal Component Analysis for Support Vector Machines

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuxian Huang ◽  
Geng Yang ◽  
Yahong Xu ◽  
Hao Zhou
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Differential Privacy ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Support Vector ◽  
Data Sets ◽  
Vector Machines ◽  
Fast Classification

In big data era, massive and high-dimensional data is produced at all times, increasing the difficulty of analyzing and protecting data. In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data. Moreover, support vector machine (SVM) is used to measure the availability of processed data in our paper. Specifically, we introduced differential privacy mechanisms at different stages of the algorithm PCA-SVM and obtained the algorithms DPPCA-SVM and PCADP-SVM. Both algorithms satisfy ε , 0 -DP while achieving fast classification. In addition, we evaluate the performance of two algorithms in terms of noise expectation and classification accuracy from the perspective of theoretical proof and experimental verification. To verify the performance of DPPCA-SVM, we also compare our DPPCA-SVM with other algorithms. Results show that DPPCA-SVM provides excellent utility for different data sets despite guaranteeing stricter privacy.

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