Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F23%3A43925825" target="_blank" >RIV/00064173:_____/23:43925825 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11120/23:43925825 RIV/00216275:25530/23:39920271 RIV/60461373:22340/23:43928395
Result on the web
<a href="https://doi.org/10.1007/s44196-023-00280-z" target="_blank" >https://doi.org/10.1007/s44196-023-00280-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s44196-023-00280-z" target="_blank" >10.1007/s44196-023-00280-z</a>
Alternative languages
Result language
angličtina
Original language name
Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM
Original language description
Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the progress of the so-called vestibular compensation to observe the rehabilitation process objectively. Currently, the quantification of their progress is highly dependent on the physician's opinion. In this article, we designed a novel methodology to classify the gait disorders associated with unilateral vestibular deafferentation in patients undergoing vestibular schwannoma surgery (model of complete vestibular loss associated with imbalance due to vestibular nerve section and eventual labyrinthectomy). We present a dual-head attentional transformer-LSTM (DHAT-LSTM) to evaluate the problem of rehabilitation from gait dysfunction, which is observed by a Kinect. A system consisting of a key-point-RCNN detector is used to compute body landmark measures and evaluate gait dysfunction based on a DHAT-LSTM network. This structure is used to quantitatively assess gait classification by tracking skeletal features based on the temporal variation of feature sequences. The proposed deep network analyses the features of the patient's movement. These extracted high-level representations are then fed to the final evaluation of gait dysfunction. The result analytically demonstrates its effectiveness in classification evaluation when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. An accuracy greater than 81% was achieved for given sets of individuals using velocity-based, angle-based, and position features for both the whole body and the symmetric features of the body.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30206 - Otorhinolaryngology
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computational Intelligence Systems
ISSN
1875-6891
e-ISSN
1875-6883
Volume of the periodical
16
Issue of the periodical within the volume
June
Country of publishing house
GB - UNITED KINGDOM
Number of pages
18
Pages from-to
98
UT code for WoS article
001002707500001
EID of the result in the Scopus database
2-s2.0-85161086146