Advanced Analysis of 3D Kinect Data: Supervised Classification of Facial Nerve Function via Parallel Convolutional Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43925420" target="_blank" >RIV/60461373:22340/22:43925420 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216275:25530/22:39919637

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/12/12/5902/pdf" target="_blank" >https://www.mdpi.com/2076-3417/12/12/5902/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app12125902" target="_blank" >10.3390/app12125902</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Analysis of 3D Kinect Data: Supervised Classification of Facial Nerve Function via Parallel Convolutional Neural Networks

Popis výsledku v původním jazyce

In this paper, we designed a methodology to classify facial nerve function after head and neck surgery. It is important to be able to observe the rehabilitation process objectively after a specific brain surgery, when patients are often affected by face palsy. The dataset that is used for classification problems in this study only contains 236 measurements of 127 patients of complex observations using the most commonly used House–Brackmann (HB) scale, which is based on the subjective opinion of the physician. Although there are several traditional evaluation methods for measuring facial paralysis, they still suffer from ignoring facial movement information. This plays an important role in the analysis of facial paralysis and limits the selection of useful facial features for the evaluation of facial paralysis. In this paper, we present a triple-path convolutional neural network (TPCNN) to evaluate the problem of mimetic muscle rehabilitation, which is observed by a Kinect stereovision camera. A system consisting of three modules for facial landmark measure computation and facial paralysis classification based on a parallel convolutional neural network structure is used to quantitatively assess the classification of facial nerve paralysis by considering facial features based on the region and the temporal variation of facial landmark sequences. The proposed deep network analyzes both the global and local facial movement features of a patient’s face. These extracted high-level representations are then fused for the final evaluation of facial paralysis. The experimental results have verified the better performance of TPCNN compared to state-of-the-art deep learning networks. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

Advanced Analysis of 3D Kinect Data: Supervised Classification of Facial Nerve Function via Parallel Convolutional Neural Networks

Popis výsledku anglicky

In this paper, we designed a methodology to classify facial nerve function after head and neck surgery. It is important to be able to observe the rehabilitation process objectively after a specific brain surgery, when patients are often affected by face palsy. The dataset that is used for classification problems in this study only contains 236 measurements of 127 patients of complex observations using the most commonly used House–Brackmann (HB) scale, which is based on the subjective opinion of the physician. Although there are several traditional evaluation methods for measuring facial paralysis, they still suffer from ignoring facial movement information. This plays an important role in the analysis of facial paralysis and limits the selection of useful facial features for the evaluation of facial paralysis. In this paper, we present a triple-path convolutional neural network (TPCNN) to evaluate the problem of mimetic muscle rehabilitation, which is observed by a Kinect stereovision camera. A system consisting of three modules for facial landmark measure computation and facial paralysis classification based on a parallel convolutional neural network structure is used to quantitatively assess the classification of facial nerve paralysis by considering facial features based on the region and the temporal variation of facial landmark sequences. The proposed deep network analyzes both the global and local facial movement features of a patient’s face. These extracted high-level representations are then fused for the final evaluation of facial paralysis. The experimental results have verified the better performance of TPCNN compared to state-of-the-art deep learning networks. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LTAIN19007" target="_blank" >LTAIN19007: Vývoj pokročilých výpočetních algoritmů pro objektivní posouzení pooperační rehabilitace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

12

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

nestrankovano

Kód UT WoS článku

000816389800001

EID výsledku v databázi Scopus

2-s2.0-85132102187