Gait disorder classification based on effective feature selection and unsupervised methodology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F24%3A43926615" target="_blank" >RIV/00064173:_____/24:43926615 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/24:43926615 RIV/00216275:25530/24:39922650 RIV/60461373:22340/24:43930907

Výsledek na webu

<a href="https://doi.org/10.1016/j.compbiomed.2024.108077" target="_blank" >https://doi.org/10.1016/j.compbiomed.2024.108077</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2024.108077" target="_blank" >10.1016/j.compbiomed.2024.108077</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gait disorder classification based on effective feature selection and unsupervised methodology

Popis výsledku v původním jazyce

In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients&apos; progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients&apos; progress. The dataset in this study includes 84 measurements of 37 patients based on a physician&apos;s opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key-point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator-discriminator networks (ET-GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high-level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi-loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.

Název v anglickém jazyce

Gait disorder classification based on effective feature selection and unsupervised methodology

Popis výsledku anglicky

In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients&apos; progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients&apos; progress. The dataset in this study includes 84 measurements of 37 patients based on a physician&apos;s opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key-point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator-discriminator networks (ET-GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high-level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi-loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30206 - Otorhinolaryngology

Projekt

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

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

1879-0534

Svazek periodika

170

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

108077

Kód UT WoS článku

001175043500001

EID výsledku v databázi Scopus

2-s2.0-85184064424