scholarly journals Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 103
Author(s):  
Jan Kohout ◽  
Ludmila Verešpejová ◽  
Pavel Kříž ◽  
Lenka Červená ◽  
Karel Štícha ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Functional Data ◽  
Muscle Function ◽  
Evaluation Process ◽  
Multiple Logistic Regression ◽  
Facial Paresis ◽  
Kinect Camera ◽  
3D Camera ◽  
Mimetic Muscles ◽  
Data Analytic

An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House–Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician’s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House–Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation.

Variation and Evolution of Function-Valued Traits

2018 ◽  
Vol 49 (1) ◽  
pp. 139-164 ◽  
Author(s):  
Richard Gomulkiewicz ◽  
Joel G. Kingsolver ◽  
Patrick A. Carter ◽  
Nancy Heckman
Keyword(s):  
Statistical Analysis ◽  
Conceptual Understanding ◽  
Functional Data ◽  
Evolutionary Dynamics ◽  
Natural Populations ◽  
Worked Examples ◽  
Heritable Variation ◽  
Continuous Index

Function-valued traits—phenotypes whose expression depends on a continuous index (such as age, temperature, or space)—occur throughout biology and, like any trait, it is important to understand how they vary and evolve. Although methods for analyzing variation and evolution of function-valued traits are well developed, they have been underutilized by evolutionists, especially those who study natural populations. We seek to summarize advances in the study of function-valued traits and to make their analyses more approachable and accessible to biologists who could benefit greatly from their use. To that end, we explain how curve thinking benefits conceptual understanding and statistical analysis of functional data. We provide a detailed guide to the most flexible and statistically powerful methods and include worked examples (with R code) as supplemental material. We review ways to characterize variation in function-valued traits and analyze consequences for evolution, including constraint. We also discuss how selection on function-valued traits can be estimated and combined with estimates of heritable variation to project evolutionary dynamics.

Nodal screw compressor failure analysis using data analytics—CSG application

2015 ◽  
Vol 55 (1) ◽  
pp. 59
Author(s):  
Prashant Parulekar
Keyword(s):  
Statistical Analysis ◽  
Data Analytics ◽  
Screw Compressor ◽  
Cause Analysis ◽  
Root Cause ◽  
Statistical Measures ◽  
Using Data ◽  
Filter Signal ◽  
Data Analytic ◽  
Insight Into

An engine-driven oil-injected screw compressor in CSG service failed catastrophically. Instrumentation provided on the package was ineffective in predicting or detecting the failure. As part of the Root Cause Analysis (RCA) process, a statistical analysis of the logged instrument data, as measured across a period of six months prior to the failure, was carried out. This paper uses data analytic methods to process instrument data, data visualisation techniques, advanced statistical analysis of the instrument data, and techniques to filter signal noise. The analysis recognised the multivariate behaviour and interrelationships between various operating parameters. The paper further provides insight into the interpretation of statistical measures and how to draw conclusions that explain the failure mechanism. The outcomes of the analysis presented in this paper then provided insights into establishing operating envelopes, proposed instrumentation upgrades to be provided in future and helped establish an operation and maintenance regime that should assist in preventing such failures in future.

A Functional Data—Analytic Approach to Signal Discrimination

Technometrics ◽  
2001 ◽  
Vol 43 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Peter Hall ◽  
D S Poskitt ◽  
Brett Presnell
Keyword(s):  
Functional Data ◽  
Analytic Approach ◽  
Signal Discrimination ◽  
Data Analytic

scholarly journals Probable Association of Hearing Loss, Hypertension and Diabetes Mellitus in the Elderly

2017 ◽  
Vol 22 (04) ◽  
pp. 337-341 ◽  
Author(s):  
Caroline Meneses-Barriviera ◽  
Jéssica Bazoni ◽  
Marcelo Doi ◽  
Luciana Marchiori
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Hearing Loss ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Pure Tone Audiometry ◽  
The Elderly ◽  
Multiple Logistic Regression ◽  
Cross Sectional ◽  
Significant Difference

Introduction The aging process causes changes in body structure in a continuous manner, and contributes to clinical disorders. Life expectancy is increasing, especially in developing countries. Objective To assess the prevalence of hearing loss and its possible association with hypertension and diabetes mellitus (DM) in the elderly. Methods A cross-sectional study with 519 elderly individuals aged over 60 years who underwent an audiological evaluation (pure tone audiometry), and answered a comorbidity questionnaire that included questions about age, gender, tinnitus and medical history, with data concerning DM. The dependent variable was the presence of hearing loss. The independent variables were age, gender, DM and hypertension. The variables were presented in absolute numbers and proportions, and enabled us to estimate the prevalence. The statistical analysis was performed through multiple logistic regression with 95% confidence intervals and values of p < 0.05 for the hearing loss and its associated factors. Results A total of 519 subjects of both genders with a median age of 69 years were evaluated, and the individuals who did not attend the audiometric test were excluded from the study, so the final sample was composed of 498 subjects. Sensorineural hearing loss was more prevalent (66.26%) of most frequently with bilateral hearing loss of 91.56% and 26.50% with mild degree. The statistical analysis showed that the variable DM was associated with the high frequency of hearing loss in the elderly, and according to the multiple logistic regression, the risk factors are independent of the hearing loss only for age and exposure to occupational noise. Conclusions There was a statistically significant difference between hearing loss at high frequencies and the risk factors, that is, age and DM.

Sign in / Sign up

Export Citation Format

Share Document