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Shayestegan M, Kohout J, Trnková K, Chovanec M, Mareš J. Gait disorder classification based on effective feature selection and unsupervised methodology. Comput Biol Med 2024; 170:108077. [PMID: 38306777 DOI: 10.1016/j.compbiomed.2024.108077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/10/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024]
Abstract
In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients' progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients' progress. The dataset in this study includes 84 measurements of 37 patients based on a physician's opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key-point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator-discriminator networks (ET-GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high-level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi-loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.
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Affiliation(s)
- Mohsen Shayestegan
- University of Pardubice, Faculty of Electrical Engineering and Informatics, Nam. Cs. Legii 565, Pardubice, 530 02, Czech Republic.
| | - Jan Kohout
- University of Chemistry and Technology Prague, Czech Republic, Department of Mathematics, Informatics and Cybernetics, Technická 1905/5, Prague, 166 28, Czech Republic.
| | - Kateřina Trnková
- Charles University Prague, 3rd Faculty of Medicine, Department of Otorhinolaryngology, University Hospital Kralovske Vinohrady, Šrobárova 1150/50, Prague, 100 34, Czech Republic.
| | - Martin Chovanec
- Charles University Prague, 3rd Faculty of Medicine, Department of Otorhinolaryngology, University Hospital Kralovske Vinohrady, Šrobárova 1150/50, Prague, 100 34, Czech Republic.
| | - Jan Mareš
- University of Pardubice, Faculty of Electrical Engineering and Informatics, Nam. Cs. Legii 565, Pardubice, 530 02, Czech Republic; University of Chemistry and Technology Prague, Czech Republic, Department of Mathematics, Informatics and Cybernetics, Technická 1905/5, Prague, 166 28, Czech Republic.
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Geradts Z, Riphagen Q. Interpol review of forensic video analysis, 2019-2022. Forensic Sci Int Synerg 2023; 6:100309. [PMID: 36632194 PMCID: PMC9827387 DOI: 10.1016/j.fsisyn.2022.100309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Zeno Geradts
- Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, the Netherlands
- University of Amsterdam, Institute for Informatics, the Netherlands
| | - Quinten Riphagen
- Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, the Netherlands
- University of Twente, the Netherlands
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The effect of viewing angle on observations of foot orientation in forensic gait analysis. Sci Justice 2020; 60:504-511. [PMID: 33077033 DOI: 10.1016/j.scijus.2020.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/13/2020] [Accepted: 06/21/2020] [Indexed: 11/21/2022]
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