GaitQualityAnalyzer

SEDMIDUBSKÝ, Jan, Tomáš LJUTENKO and Pavel ZEZULA. GaitQualityAnalyzer. 2022.

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

• Original language: English
• Type of outcome: Software
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• 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

Abstract

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