An index to quantify normality of gait in young children

Tingley, Maureen; Wilson, Carla; Biden, E.; Knight, William

doi:10.1016/s0966-6362(02)00012-7

Cited by 32 publications

(41 citation statements)

References 10 publications

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“…In comparison with the other tests' graphs, the foot rotation graphs were not clear enough. Even though this study was kinematic and did not include children under 5 years old, the difference between adults and children under fi ve years old in ankle kinetics was also mentioned in the literature (15,16). This supports some late maturation of ankle joint movements or moments.…”

Section: Discussionmentioning

confidence: 66%

3d Motion Analysis Of Hip, Knee And Ankle Joints During Gait For 7-11 Aged Children

Cıgalı¹,

Ulucam²,

Bozer³

2010

TUTFD

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Section: Discussionmentioning

confidence: 66%

3d Motion Analysis Of Hip, Knee And Ankle Joints During Gait For 7-11 Aged Children

Cıgalı¹,

Ulucam²,

Bozer³

2010

TUTFD

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“…Subsequently, a shrinking of toward a user-defined symmetric positive-definite matrix is obtained via (9) The effect of this step is to alter the eigenvalue profile of toward the one dictated by . A typical choice for is the identity matrix ; in this case the second sum of the right-side of (9) corresponds to a spherical covariance of radius relative to the mean within-class variance.…”

Section: Covariance Regularizationmentioning

confidence: 99%

“…A dynamic recurrent neural network was employed in [7] to predict kinematic variables from EMG data. Principal component analysis (PCA) was used in [8] to identify muscle activation patterns using surface EMG, and in [9] to assess gait normality in children using sagittal plane joint data. Additional examples include [10] who employed cluster analysis to identify abdominal and erector spinae muscle activity patterns, and [11] who used linear discriminant analysis (LDA) to differentiate between normal and flat foot subjects through the use of force measurements.…”

Section: Introductionmentioning

confidence: 99%

Automated Design of Robust Discriminant Analysis Classifier for Foot Pressure Lesions Using Kinematic Data

Goulermas

Findlow

Nester

et al. 2005

IEEE Trans. Biomed. Eng.

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Abstract-In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap 632+ and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to 96% correct classification rates with less than 10% of the original features.

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“…Waveform classifiers attempt to capture the entire gait curve. Such methods may include Fourier analysis [7,8], bootstrap methods [8,9], principal components analysis [10][11][12], and neural network and pattern recognition techniques [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%