Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920921" target="_blank" >RIV/00216275:25530/23:39920921 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICECCME57830.2023.10252459" target="_blank" >http://dx.doi.org/10.1109/ICECCME57830.2023.10252459</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICECCME57830.2023.10252459" target="_blank" >10.1109/ICECCME57830.2023.10252459</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

Popis výsledku v původním jazyce

This paper presents a new methodology for the data processing and classification method for gait disorders, which is observed with a Kinect camera. The study of gait and motion stability in gait disorders is one of the most interesting research areas in the field. The patient and the physician must monitor the progress of the rehabilitation process before and after surgery to obtain an objective view of the rehabilitation process. In this study, the patient is scanned with the Kinect camera placed on a mobile robotic platform. For feature extraction and feature analysis, the exercises (three walking exercises) frames are collected and saved in data folders. This study uses 84 measurements of 37 patients with complex observations based on the physician&apos;s opinion in a clinical setting to address classification problems. In the analysis of gait disorders, motion data play an essential role. Furthermore, it reduces the selection of helpful body features for assessing gait disorders. The proposed system uses a key-point detector that computes body landmarks and classifies gait disorders using triple-parallel long short-term memory (LSTM) networks. The present study demonstrates the success of the method in classification evaluation when combined with the state-of-the-art pose estimation method. Around 81 percent accuracy was achieved for given sets of individuals using velocity-based, angle-based, and position-based features.

Název v anglickém jazyce

Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

Popis výsledku anglicky

This paper presents a new methodology for the data processing and classification method for gait disorders, which is observed with a Kinect camera. The study of gait and motion stability in gait disorders is one of the most interesting research areas in the field. The patient and the physician must monitor the progress of the rehabilitation process before and after surgery to obtain an objective view of the rehabilitation process. In this study, the patient is scanned with the Kinect camera placed on a mobile robotic platform. For feature extraction and feature analysis, the exercises (three walking exercises) frames are collected and saved in data folders. This study uses 84 measurements of 37 patients with complex observations based on the physician&apos;s opinion in a clinical setting to address classification problems. In the analysis of gait disorders, motion data play an essential role. Furthermore, it reduces the selection of helpful body features for assessing gait disorders. The proposed system uses a key-point detector that computes body landmarks and classifies gait disorders using triple-parallel long short-term memory (LSTM) networks. The present study demonstrates the success of the method in classification evaluation when combined with the state-of-the-art pose estimation method. Around 81 percent accuracy was achieved for given sets of individuals using velocity-based, angle-based, and position-based features.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

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 statě ve sborníku

International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 : proceedings

ISBN

979-8-3503-2298-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Tenerife

Datum konání akce

19. 7. 2023

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—