Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F24%3A43926715" target="_blank" >RIV/00064173:_____/24:43926715 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/24:43926715 RIV/00216275:25530/24:39922651 RIV/60461373:22340/24:43930904

Výsledek na webu

<a href="https://doi.org/10.1109/ACCESS.2024.3360861" target="_blank" >https://doi.org/10.1109/ACCESS.2024.3360861</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2024.3360861" target="_blank" >10.1109/ACCESS.2024.3360861</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders

Popis výsledku v původním jazyce

Recently, systems for classifying gait disorders have been of great interest. However, quantifying the progress of these disorders has been highly dependent on a physician&apos;s judgement in classifying sick and healthy subjects. We examine the effects of gait stability analysis on gait dysfunction problems, which are impacted by the patient&apos;s dynamic balance. The dataset in this study was collected and labelled based on the opinions of physicians at Prague Hospital; it included 84 measurements of 37 patients. A keypoint detector was applied to detect the skeletal keypoints of patients. We have prepared two different datasets from the detection and tracking results. For the proposed feature selection method, we have used statistical measurements such as the x and y coordinates for each keypoint, the distance, and the angle between two selected keypoints. Using these statistical measurements, we have prepared different subgroups with different numbers of features to examine. We have also applied ten different feature selection algorithms to obtain data from different numbers of features automatically. Then, these datasets with high-level features were used to train well-known networks, such as the long short-term memory (LSTM), gated recurrent unit (GRU), and multiple layer perceptron (MLP) networks. The study results showed that the 30 features selected by the analysis of variance (ANOVA) algorithm and used to train the GRU network ranked among the best features and resulted in a classification F -score of 85%. The results also prove that the data generated by the detector method are more effective than the data generated by the tracking method due to the format of the exercises in our dataset, which were designed by physicians. Moreover, the best feature selection approaches have considerably improved the classification F -score compared to manual feature generation.

Název v anglickém jazyce

Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders

Popis výsledku anglicky

Recently, systems for classifying gait disorders have been of great interest. However, quantifying the progress of these disorders has been highly dependent on a physician&apos;s judgement in classifying sick and healthy subjects. We examine the effects of gait stability analysis on gait dysfunction problems, which are impacted by the patient&apos;s dynamic balance. The dataset in this study was collected and labelled based on the opinions of physicians at Prague Hospital; it included 84 measurements of 37 patients. A keypoint detector was applied to detect the skeletal keypoints of patients. We have prepared two different datasets from the detection and tracking results. For the proposed feature selection method, we have used statistical measurements such as the x and y coordinates for each keypoint, the distance, and the angle between two selected keypoints. Using these statistical measurements, we have prepared different subgroups with different numbers of features to examine. We have also applied ten different feature selection algorithms to obtain data from different numbers of features automatically. Then, these datasets with high-level features were used to train well-known networks, such as the long short-term memory (LSTM), gated recurrent unit (GRU), and multiple layer perceptron (MLP) networks. The study results showed that the 30 features selected by the analysis of variance (ANOVA) algorithm and used to train the GRU network ranked among the best features and resulted in a classification F -score of 85%. The results also prove that the data generated by the detector method are more effective than the data generated by the tracking method due to the format of the exercises in our dataset, which were designed by physicians. Moreover, the best feature selection approaches have considerably improved the classification F -score compared to manual feature generation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30206 - Otorhinolaryngology

Projekt

—

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

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

12

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

17876-17894

Kód UT WoS článku

001161864000001

EID výsledku v databázi Scopus

2-s2.0-85184335149