A novel approach to study human posture control: “Principal movements” obtained from a principal component analysis of kinematic marker data

The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.

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Medium term load forecasting in distribution systems based on multi linear regression & principal component analysis: A novel approach

2014 19th Conference on Electrical Power Distribution Networks (EPDC) ◽

10.1109/epdc.2014.6867500 ◽

2014 ◽

Cited By ~ 7

Author(s):

Roozbeh Torkzadeh ◽

Ahmad Mirzaei ◽

Mohammad Mehdi Mirjalili ◽

Alireza Sedighi Anaraki ◽

Mohammad Reza Sehhati ◽

...

Keyword(s):

Principal Component Analysis ◽

Linear Regression ◽

Distribution Systems ◽

Load Forecasting ◽

Principal Component ◽

Component Analysis ◽

Medium Term ◽

Novel Approach ◽

Multi Linear Regression

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Characterising 3D spherical packings by principal component analysis

Engineering Computations ◽

10.1108/ec-05-2019-0225 ◽

2019 ◽

Vol 37 (3) ◽

pp. 1023-1041 ◽

Cited By ~ 1

Author(s):

Tingting Zhao ◽

Y.T. Feng ◽

Yuanqiang Tan

Keyword(s):

Principal Component Analysis ◽

Gaussian Quadrature ◽

Three Dimensional ◽

Principal Component ◽

Component Analysis ◽

Particle Packing ◽

Cross Sectional ◽

Content Type ◽

Computer Methods ◽

Novel Approach

Purpose The purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional. Design/methodology/approach Gaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images). Findings Differences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach. Originality/value Develop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences.

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