Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11120/23:43925825 RIV/00216275:25530/23:39920271 RIV/60461373:22340/23:43928395

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM

Popis výsledku v původním jazyce

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&apos;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&apos;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.

Název v anglickém jazyce

Motion Tracking in Diagnosis: Gait Disorders Classification with a Dual-Head Attentional Transformer-LSTM

Popis výsledku anglicky

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&apos;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&apos;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.

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

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computational Intelligence Systems

ISSN

1875-6891

e-ISSN

1875-6883

Svazek periodika

16

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

98

Kód UT WoS článku

001002707500001

EID výsledku v databázi Scopus

2-s2.0-85161086146