R 2022

GaitQualityAnalyzer

SEDMIDUBSKÝ, Jan, Tomáš LJUTENKO and Pavel ZEZULA

Basic information

Original name

GaitQualityAnalyzer

Authors

SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution), Tomáš LJUTENKO (703 Slovakia, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

2022

Other information

Language

English

Type of outcome

Software

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

RIV identification code

RIV/00216224:14330/22:00127383

Organization unit

Faculty of Informatics

Keywords in English

3D motion data;gait cycle;similarity search;gait quality assessment

Technical parameters

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.

Tags

International impact
Změněno: 10/3/2023 13:12, doc. RNDr. Jan Sedmidubský, Ph.D.

Abstract

V originále

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.

Links

MUNI/G/1585/2019, interní kód MU
Name: Improving Treatments in Cerebral-Palsy Children using Artificial Intelligence
Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects