Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10434985" target="_blank" >RIV/00216208:11320/21:10434985 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22340/21:43923548 RIV/00064173:_____/21:N0000169

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=owihRbyy7N" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=owihRbyy7N</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app11104572" target="_blank" >10.3390/app11104572</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

Popis výsledku v původním jazyce

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House-Brackmann (HB) scale is based on the clinician&apos;s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician&apos;s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6-it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.

Název v anglickém jazyce

Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

Popis výsledku anglicky

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House-Brackmann (HB) scale is based on the clinician&apos;s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician&apos;s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6-it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Sciences [online]

ISSN

2076-3417

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

4572

Kód UT WoS článku

000662587300001

EID výsledku v databázi Scopus

2-s2.0-85106946568