Applied Biomechatroncis Using Mathematicals Models Dataset Chapter 5

Published: 07-07-2018| Version 1 | DOI: 10.17632/6nw9crk4j9.1
Contributor:
Jorge Garza-Ulloa

Description

Mathematical models for kinematics, kinetics, and muscles potentials activities from sEMG based on traditional statistical analysis are developed using different methods for data analysis, where each model is represented using a structure with a linear dynamic form, explicit and discrete, that can be verified as stochastic process and arising from empirical finding. In this chapter, Mathematical tools are studied with the objective of obtaining Mathematical Models from: traditional stochastic methods from probability and statistics as probability models, probability distributions, statistical inferences using statistical hypotheses testing parameters, z-tests, t-tests, paired t-tests, ANOVA. We apply them to: Linear equations, Regression methods, and Autoregressive equations. The different methods explained are applied to research Biomechanics examples to model and detect data behaviors, and this chapter is concluded with the development of a special software application of Mathematical Models for Analysis of Continuous Glucose Monitor (CGM) for Diabetic subjects. Note: Others Mathematical Models based on Domain/Conversion/Transform analysis, and Machine Learning Models Analysis are studied in the next chapters.

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Steps to reproduce

Follow examples of chapter 5 in the book Applied Biomechatronics Using Mathemathical Models. More information on www.garzaulloa.org