In this study, the effectiveness of Linear Discriminant Analysis (LDA) as feature extraction and dimensionality reduction is evaluated and compared with Singular Value Decomposition (SVD) for gait recognition of Parkinson Disease subjects as compared to normal subjects. Here, three feature vectors of gait namely basic, kinetic and kinematic features are extracted and analysed using LDA and leave one out (LOO) recognition method. Next, ANN classifier is used to compare the performance of LDA versus SVD. Initial findings confirmed that the gait pattern between PD and normal subjects can be classified using the three feature vectors with kinematic features outperformed basic spatial-temporal and kinetic feature vectors with 86.71% with ANN as classifier and 87.5% using LOO recognition technique.

Comparison between Linear Discriminant Analysis and Singular Value Decomposition for PD gait classification

Recently, gait patterns of children with autism is of interest in the gait community in order to identify significant gait parameter namely the three dimensional (3D) gait features such as spatial temporal, kinematic and kinetic. This is because gait pattern provides clinicians and researchers in understanding the trajectory of gait development. Understanding the characteristics and identifying gait pattern is essential in order to distinguish normal as well as abnormal gait pattern. Hence the purpose of this review is to identify deviations gait in children with autism based on criteria specifically subject character; measurement, type of gait variables measured; method of classification and major findings. Several gait variables from different instrumentation for gait analysis is reviewed too. Development of gait patterns via assessing gait deviations in children with ASD could assist clinician and researchers to differentiate gait pattern abnormality in diagnosing, clinical decision-making and treatment planning as well.

Development of three dimensional gait pattern in autism children - a review

In order to extract the gait sequence features better, this paper presents a method of feature extraction algorithm based on Fisher Discriminance. First, extract the profile silhouette by background cut off method, and then get the gait cycle by the change of their center of mass, at last convert the feature space. Then weight the gait characteristic matrix by Fisher Discriminance to make characteristics of the gait more obvious. Finally, test the algorithm by Nearest Neighbor Method.The results show that this algorithm has a good performance of recognition.

http://ieeexplore.ieee.org/iel7/6483173/6491698/06491800.pdf

Body gait tracking with a Fisher Discriminance on feature analyse

In this paper we deal with the tracking problem of human hand. The algorithm consists of two steps: At first the move part is detected and recognized with a advanced DOF model; then the curve tracking analysis is applied to get the key features with more details. The camera based on computer vision has the potential to provide much more natural, non-contact tracking. Although move part such as arms has a more faster speed, it can also been located exactly. With the semantic breakpoints extracted, some active part in a complex background can be location at any time in a test period.

A novel uncalibrated human hand tracking based on transition model

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Body gait tracking with a Fisher Discriminance on feature analyse

A novel uncalibrated human hand tracking based on transition model