IDENTIFICATION OF KNEE FRONTAL PLANE KINEMATIC PATTERNS IN NORMAL GAIT BY PRINCIPAL COMPONENT ANALYSIS

2013 ◽  
Vol 13 (03) ◽  
pp. 1350026 ◽  
Author(s):  
NEILA MEZGHANI ◽  
ALEXANDRE FUENTES ◽  
NATHALY GAUDREAULT ◽  
AMAR MITICHE ◽  
RACHID AISSAOUI ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Stance Phase ◽  
Large Population ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Swing Phase ◽  
Statistical Hypothesis ◽  
Gait Patterns ◽  
Phase Cluster

The purpose of this study was to identify meaningful gait patterns in knee frontal plane kinematics from a large population of asymptomatic individuals. The proposed method used principal component analysis (PCA). It first reduced the data dimensionality, without loss of relevant information, by projecting the original kinematic data onto a subspace of significant principal components (PCs). This was followed by a discriminant model to separate the individuals' gait into homogeneous groups. Four descriptive gait patterns were identified and validated by clustering silhouette width and statistical hypothesis testing. The first pattern was close to neutral during the stance phase and in adduction during the swing phase (Cluster 1). The second pattern was in abduction during the stance phase and tends into adduction during the swing phase (Cluster 2). The third pattern was close to neutral during the stance phase and in abduction during the swing phase (Cluster 3) and the fourth was in abduction during both the stance and the swing phase (Cluster 4).

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.

scholarly journals Comparison of different marker sets for marker trajectory and principal component analysis based classification of simulated gait impairments

2016 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Josef Christian ◽  
Felix Kluge ◽  
Björn M Eskofier ◽  
Hermann Schwameder
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Specific Marker ◽  
Gait Patterns ◽  
Gait Classification ◽  
Functional Aspects ◽  
Improved Performance ◽  
High Level ◽  
Gait Impairments

Objective: Many different marker sets have been used in marker trajectory based gait classification approaches. Little knowledge exists about the effects of specific marker sets on the subsequent statistical modeling. Such analysis is often based on principal component analysis. The aim of this study was to test the effect of marker set choice on marker trajectory and principal component analysis based gait classification. Methods: This study tested the performance of principal component analysis based gait classification models with various marker sets on the basis of simulated gait impairments. Simulated gait impairments were used to enable a high level of control of the gait patterns. Results: Classification accuracies were similar across most tested marker sets. Improved performance could be detected for some marker sets depending on the type of impairment. Conclusion: Several potentially valid marker sets exist for a specific gait classification task even though trends could be found suggesting that optimal marker set choice is dependent on functional aspects of the movement.

scholarly journals Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

2016 ◽  
Vol 101 ◽  
pp. 45-54 ◽  
Author(s):  
Francesc Pozo ◽  
Yolanda Vidal
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Hypothesis Testing ◽  
Wind Turbine ◽  
Principal Component ◽  
Component Analysis ◽  
Angle Measurement ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Detection Scheme

This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.

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