scholarly journals Development of Machine Learning Algorithms for the Determination of the Centre of Mass

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 401
Author(s):  
Danilo D’Andrea ◽  
Filippo Cucinotta ◽  
Flavio Farroni ◽  
Giacomo Risitano ◽  
Dario Santonocito ◽  
...  
Keyword(s):  
Human Body ◽  
Machine Learning Algorithms ◽  
Vertical Position ◽  
Centre Of Mass ◽  
Wii Balance Board ◽  
Almost All ◽  
Mass Position ◽  
Balance Board ◽  
Capture Technique

The study of the human body and its movements is still a matter of great interest today. Most of these issues have as their fulcrum the study of the balance characteristics of the human body and the determination of its Centre of Mass. In sports, a lot of attention is paid to improving and analysing the athlete's performance. Almost all the techniques for determining the Centre of Mass make use of special sensors, which allow determining the physical magnitudes related to the different movements made by athletes. In this paper, a markerless method for determining the Centre of Mass of a subject has been studied, comparing it with a direct widely validated equipment such as the Wii Balance Board, which allows determining the coordinates of the Centre of Pressure. The Motion Capture technique was applied with the OpenPose software, a Computer Vision method boosted with the use of Convolution Neural Networks. Ten quasi-static analyses have been carried out. The results have shown an error of the Centre of Mass position, compared to that obtained from the Wii Balance Board, which has been considered acceptable given the complexity of the analysis. Furthermore, this method, despite the traditional methods based on the use of balances, can be used also for prediction of the vertical position of the Centre of Mass.

A fast methodology to determine the characteristics of thousands of grains using three-dimensional X-ray diffraction. I. Overlapping diffraction peaks and parameters of the experimental setup

2012 ◽  
Vol 45 (4) ◽  
pp. 693-704 ◽  
Author(s):  
Hemant Sharma ◽  
Richard M. Huizenga ◽  
S. Erik Offerman
Keyword(s):  
Data Analysis ◽  
Three Dimensional ◽  
Experimental Setup ◽  
Centre Of Mass ◽  
X Ray Diffraction ◽  
X Ray ◽  
Overlapping Peaks ◽  
Diffraction Peaks ◽  
Mass Position

A data-analysis methodology is presented for the characterization of three-dimensional microstructures of polycrystalline materials from data acquired using three-dimensional X-ray diffraction (3DXRD). The method is developed for 3DXRD microscopy using a far-field detector and yields information about the centre-of-mass position, crystallographic orientation, volume and strain state for thousands of grains. This first part deals with pre-processing of the diffraction data for input into the algorithms presented in the second part [Sharma, Huizenga & Offerman (2012).J. Appl. Cryst.45, 705–718] for determination of the grain characteristics. An algorithm is presented for accurate identification of overlapping diffraction peaks from X-ray diffraction images, which has been an issue limiting the accuracy of experiments of this type. The algorithm works in two stages, namely the identification of overlapping peaks using a seeded watershed algorithm, and then the fitting of the peaks with a pseudo-Voigt shape function to yield an accurate centre-of-mass position and integrated intensity for the peaks. Regions consisting of up to six overlapping peaks can be successfully fitted. Two simulations and an experiment are used to verify the results of the algorithms. An example of the processing of diffraction images acquired in a 3DXRD experiment with a sample consisting of more than 1600 grains is shown. Furthermore, a procedure for the determination of the parameters of the experimental setup (global parameters) without the need for a calibration sample is presented and validated using simulations. This is immensely beneficial for simplifying experiments and the subsequent data analysis.

A fast methodology to determine the characteristics of thousands of grains using three-dimensional X-ray diffraction. II. Volume, centre-of-mass position, crystallographic orientation and strain state of grains

2012 ◽  
Vol 45 (4) ◽  
pp. 705-718 ◽  
Author(s):  
Hemant Sharma ◽  
Richard M. Huizenga ◽  
S. Erik Offerman
Keyword(s):  
Crystallographic Orientation ◽  
Strain State ◽  
Strain Tensor ◽  
Three Dimensional ◽  
Centre Of Mass ◽  
X Ray Diffraction ◽  
Computation Efficiency ◽  
Experimental Errors ◽  
Mass Position

This second part of the paper on an analysis strategy for data acquired using three-dimensional X-ray diffraction (3DXRD) describes the procedure for the determination of the grain characteristics for thousands of grains. The approach developed here is orders of magnitude faster than those presently available for indexing thousands of grains. Using information obtained from the steps described in Part I [Sharma, Huizenga & Offerman (2012).J. Appl. Cryst.45, 693–704], the volume, crystallographic orientation, centre-of-mass position and strain state of grains in the sample can be determined. The algorithms dealing with the determination of the orientation, centre-of-mass position and strain state of the grains are divided into two parts. The first deals with indexing,i.e.it assigns diffraction spots to individual grains assuming an unstrained lattice, and the second deals with the refinement of the crystallographic orientation, centre-of-mass position and strain state of the grains using the diffraction spots assigned during indexing. The different approaches to indexing that exist in the literature are presented and compared with the novel approach developed here. Indexing can be run in two modes depending on the number of grains. For large numbers of grains, the approach employs a novel sample `surface' scanning technique, in combination with a reduced number of search orientations, to achieve high robustness and computation efficiency. For small numbers of grains, the approach neglects the position of the diffracting grains in the sample in order to improve the computation efficiency. For unstrained samples, both modes of indexing and the subsequent process of refinement are validated using simulated data for 60 and 3000 grains. In both cases, the centre-of-mass position of the grains was determined with a mean error of 0.7 µm and the orientation was determined with a mean error of 0.0003°. Furthermore, an experiment was `mimicked' by introducing experimental errors into the simulation for 3000 grains. The resulting mean errors in the centre-of-mass position (2.1 µm) and orientation (0.008°) of the grains are higher than those for the ideal simulations, and the errors in an experiment will depend on the `true' experimental errors. The algorithms dealing with strained samples are validated using a simulation for 3000 grains with mimicked experimental errors. The centre-of-mass position, crystallographic orientation, normal strain tensor components and shear strain tensor components of the grains were determined with mean errors of 8 µm, 0.006°, 5.2 × 10−5and 2.8 × 10−5, respectively. The possibility of obtaining grain-level information for thousands of grains with a high speed of acquisition makes the technique very attractive forin situstudies of thermomechanical processes in polycrystalline materials.

Population Change, Modernization and Welfare. By Joseph Spengler. Prentice-Hall Inc., Englewood Cliffs, N.J. 1974.

1977 ◽  
Vol 16 (2) ◽  
pp. 220-222
Author(s):  
Zeba A. Sathar
Keyword(s):  
Population Growth ◽  
Population Change ◽  
Population Control ◽  
Stable Condition ◽  
Developed Countries ◽  
Wide Field ◽  
Wide Scale ◽  
Socio Economic Variables ◽  
Almost All

The book covers a wide field, touching on almost all aspects of popula¬tion change on a world-wide scale. It discusses, using world and country data, the relationships between demographic and socio-economic variables, and elaborates on" their relative importance in the determination of population problems which confront the world as a whole and nations individually. Policies designed to alleviate these problems are discussed with an emphasis on those related to population control. The first chapter is entitled "Population Growth: Past and Prospective" and reviews the various parameters associated with population change in the past and in the future. It touches upon the concept of a stable population in order to show the elements which cause a population to change (i.e. remove it from its stable condition). The main elements of change, population growth, migration, mortality and natality are discussed individually. The chapter is concluded by a description of the main differences in these elements and other socio-economic conditions as they exist in the less-developed and developed countries.

The Determination of the Noise Pollution in a Production Hall and the Diseases Produced by It

2018 ◽  
Vol 69 (4) ◽  
pp. 875-880
Author(s):  
Oana Roxana Chivu ◽  
Ovidiu Mederle ◽  
Augustin Semenescu ◽  
Ileana Mates ◽  
Claudiu Babis ◽  
...  
Keyword(s):  
Risk Factor ◽  
Human Body ◽  
Noise Level ◽  
Noise Pollution ◽  
Protective Measures ◽  
Work Program ◽  
Specialized Equipment ◽  
Chemical Products ◽  
Main Activity

The paper describes how to determine the noise in a production hall where there are production machines and equipment. The chosen plant is a production unit where filling of the polyurethane foam tubes is performed. The main activity is the production of professional insulators and wholesale of other products from the same range enumerating the following: wholesale chemical products; production of professional insulators; trade in chemicals and chemical industry. The recommended maximum admissible value for a normal 8h work program is 85 dB (A). The objective is to determine the level of noise in the factory and how it acts as a physical professional risk factor and its effects on the human body. The way in which the proposed objective was achieved was to determine the value of the noise level with the specialized equipment. Following the determinations that will be presented in the following chapters, appropriate protective measures have been taken.

Novel Technologıes and Nanotoxıcology of Medıcal Implants

2020 ◽  
Vol 16 ◽  
Author(s):  
Ramazan Akçan ◽  
Halit Canberk Aydogan ◽  
Mahmut Şerif Yıldırım ◽  
Burak Taştekin ◽  
Necdet Sağlam
Keyword(s):  
Human Health ◽  
Human Body ◽  
Diagnostic Procedures ◽  
Toxic Effects ◽  
Medical Implants ◽  
Healthcare Applications ◽  
Detailed Review ◽  
Novel Technologies ◽  
Technological Developments

Background/aim: Use of nanomaterials in the healthcare applications increases in parallel to technological developments. It is frequently utilized in diagnostic procedures, medications and in therapeutic implementations. Nanomaterials take place among key components of medical implants, which might be responsible for certain toxic effects on human health at nano-level. In this review, nanotoxicological effects, toxicity determination of nanobiomaterials used in human body and their effects on human health are discussed. Material and Method: A detailed review of related literature was performed and evaluated as per nanomaterials and medical implants. Results and Conclusion: The nanotoxic effects of the materials applied to human body and the determination of its toxicity are important. Determination of toxicity for each nanomaterial requires a detailed and multifactorial assessment considering the properties of these materials. There are limited studies in the literature regarding the toxic effects of nanomaterials used in medical implants. Although these implants are potentially biocompatible and biodegradable, it is highly important to discuss nanotoxicological characteristics of medical implant.

scholarly journals Glioblastoma and MiRNAs

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1581
Author(s):  
Swalih P. Ahmed ◽  
Javier S. Castresana ◽  
Mehdi H. Shahi
Keyword(s):  
Brain Tumors ◽  
Human Body ◽  
Diagnostic Tool ◽  
Poor Prognosis ◽  
Treatment Options ◽  
Diagnosis And Treatment ◽  
Therapeutic Success ◽  
Knowing That ◽  
Almost All ◽  
Cell Signaling Pathways

Glioblastoma (GB) is one of the most common types of lethal brain tumors. Although several treatment options are available including surgery, along with adjuvant chemo and radiotherapy, the disease has a poor prognosis and patients generally die within 14 months of diagnosis. GB is chemo and radio resistant. Thus, there is a critical need for new insights into GB treatment to increase the chance of therapeutic success. This is why microRNA (miRNA) is being potentially considered in the diagnosis and treatment of glioblastoma. The objective of our review is to provide a holistic picture of GB up-regulated and down-regulated miRNA, in relationship with the expression of other genes, cell signaling pathways, and their role in GB diagnosis and treatment. MiRNA treatment is being considered to be used against GB together with radiotherapy and chemotherapy. Moreover, the use of miRNA as a diagnostic tool has also begun. Knowing that miRNAs are isolated in almost all human body fluids and that there are more than 3000 miRNAs in the human genome, plus the fact that each miRNA controls hundreds of different mRNAs, there is still much study needed to explore how miRNAs relate to GB for its proliferation, progression, and inhibition.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

scholarly journals Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

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