Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders
The result's identifiers
Result code in 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>
Alternative codes found
RIV/00216208:11120/24:43926715 RIV/00216275:25530/24:39922651 RIV/60461373:22340/24:43930904
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders
Original language description
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'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'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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30206 - Otorhinolaryngology
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
February
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
17876-17894
UT code for WoS article
001161864000001
EID of the result in the Scopus database
2-s2.0-85184335149