Version 1
: Received: 8 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (11:02:06 CEST)
How to cite:
Gonzalez-Islas, J.-C.; Dominguez-Ramirez, O.-A.; Lopez-Ortega, O.; Pena Ramirez, J. Crouch Gait Recognition in the Anatomical Space using Synthetic Gait Data. Preprints2024, 2024100664. https://doi.org/10.20944/preprints202410.0664.v1
Gonzalez-Islas, J.-C.; Dominguez-Ramirez, O.-A.; Lopez-Ortega, O.; Pena Ramirez, J. Crouch Gait Recognition in the Anatomical Space using Synthetic Gait Data. Preprints 2024, 2024100664. https://doi.org/10.20944/preprints202410.0664.v1
Gonzalez-Islas, J.-C.; Dominguez-Ramirez, O.-A.; Lopez-Ortega, O.; Pena Ramirez, J. Crouch Gait Recognition in the Anatomical Space using Synthetic Gait Data. Preprints2024, 2024100664. https://doi.org/10.20944/preprints202410.0664.v1
APA Style
Gonzalez-Islas, J. C., Dominguez-Ramirez, O. A., Lopez-Ortega, O., & Pena Ramirez, J. (2024). Crouch Gait Recognition in the Anatomical Space using Synthetic Gait Data. Preprints. https://doi.org/10.20944/preprints202410.0664.v1
Chicago/Turabian Style
Gonzalez-Islas, J., Omar Lopez-Ortega and Jonatan Pena Ramirez. 2024 "Crouch Gait Recognition in the Anatomical Space using Synthetic Gait Data" Preprints. https://doi.org/10.20944/preprints202410.0664.v1
Abstract
Crouch gait is one of the most common gait abnormalities. In the literature, there are few works related to the crouch gait recognition. In this work, we aim at the integration of a framework for multiclass crouch gait recognition using synthetic eight-DoF model gait data in anatomical space. The main contribution of our study is the joint function generator algorithm, which could be used to generate other physiological signals; and the gait data validation stage. In addition, we use a feature selection method and a body region segmentation approach to improve classification performance. We generated a gait dataset containing 25 join functions of each DoF for each gait class (4 crouched and normal). The algorithms that we evaluated for classification were: K-nearest neighbors (kNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artificial neural network (ANN). We calculated the metrics: accuracy (Acc), recall (R), specificity (SP), precision (P) and the F-measure (FM) to asses the classification performance. In general, for this work, the best algorithms were discriminant analysis and artificial neural networks. The main results of the proposed recognition framework provide evidence that can be used in real clinical settings to diagnose and make decisions about treatments related to crouch gait diseases.
Keywords
Gait recognition; crouch gait; synthetic gait data
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
