SEDMIDUBSKÝ, Jan, Tomáš LJUTENKO a Pavel ZEZULA. GaitQualityAnalyzer. 2022.
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Základní údaje
Originální název GaitQualityAnalyzer
Autoři SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí), Tomáš LJUTENKO (703 Slovensko, domácí) a Pavel ZEZULA (203 Česká republika, domácí).
Vydání 2022.
Další údaje
Originální jazyk angličtina
Typ výsledku Software
Obor 10200 1.2 Computer and information sciences
Stát vydavatele Česká republika
Utajení není předmětem státního či obchodního tajemství
WWW URL
Kód RIV RIV/00216224:14330/22:00127383
Organizační jednotka Fakulta informatiky
Klíčová slova anglicky 3D motion data;gait cycle;similarity search;gait quality assessment
Technické parametry The technical documentation of the GaitQualityAnalyzer system, including the description of installation steps, system design, and graphical user interface, is available at the system web page.
Příznaky Mezinárodní význam
Změnil Změnil: doc. RNDr. Jan Sedmidubský, Ph.D., učo 60474. Změněno: 10. 3. 2023 13:12.
Anotace
GaitQualityAnalyzer is a system for analyzing the quality of human gait. The system manages human subjects along with their gait style recorded in different time periods in the form of motion capture data. The main system objective is to provide the functionality for searching for similar movement patterns on the level of gait cycles and assessing the quality of the retrieved patterns. This enables determining whether a subject performs better or worse after some circumstance, e.g., underwent surgery. Even if the system is intended to be used for evaluating the suitability of treatments for patients suffering from cerebral-palsy disease, it can be potentially applied to other scenarios in which the quality of movement patterns needs to be analyzed. The system is implemented as a client-server architecture with a web-based graphical user interface. The interface primarily allows users to: (1) manage subjects, visits of subjects, and gait recordings associated with visits, (2) assess the quality of gait recordings, (3) search for similar gait recordings of other subjects based on a pre-trained recurrent neural network model, and (4) determine whether the most relevant retrieved subjects exhibit an improvement or deterioration of gait style.
Návaznosti
MUNI/G/1585/2019, interní kód MUNázev: Improving Treatments in Cerebral-Palsy Children using Artificial Intelligence
Investor: Masarykova univerzita, Improving Treatments in Cerebral-Palsy Children using Artificial Intelligence, INTERDISCIPLINARY - Mezioborové výzkumné projekty
VytisknoutZobrazeno: 23. 7. 2024 20:18