The identification of individuals based on their walking patterns, also known as gait recognition, has garnered considerable interest as a biometric trait. The use of gait patterns for gender classification has emerged as a significant research domain with diverse applications across multiple fields. The present investigation centers on the classification of gender based on gait utilizing data from Ultra-wide band radar. A total of 181 participants were included in the study, and data was gathered using Ultra-wide band radar technology. This study investigates various preprocessing techniques, feature extraction methods, and dimensionality reduction approaches to efficiently process Ultra-wide band radar data. The data quality is improved through the utilization of a two-pulse canceller and discrete wavelet transform. The hybrid feature dataset is generated through the creation of gray-level co-occurrence matrices and subsequent extraction of statistical features. Principal Component Analysis is utilized for dimensionality reduction, and prediction probabilities are incorporated as features for classification optimization. The present study employs k-fold cross-validation to train and assess machine learning classifiers, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, K-Nearest Neighbors, and Extra Tree Classifier. The Multilayer Perceptron exhibits superior performance, achieving an accuracy of 0.936. The Support Vector Machine and k-Nearest Neighbors classifiers closely trail behind, both achieving an accuracy of 0.934. This research is of the utmost importance due to its capacity to offer solutions to crucial problems in multiple domains. The findings indicate that the utilization of UWB radar data for gait-based gender classification holds promise in diverse domains, including biometrics, surveillance, and healthcare. The present study makes a valuable contribution to the progress of gender classification systems that rely on gait patterns.

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基于人工智能增强超宽带雷达步态分析的性别分类

基于步行模式的个人识别（也称为步态识别）作为一种生物特征已引起了人们的广泛关注。使用步态模式进行性别分类已成为一个重要的研究领域，在多个领域具有多种应用。目前的调查重点是利用超宽带雷达的数据根据​​步态进行性别分类。该研究共有 181 名参与者，并使用超宽带雷达技术收集数据。本研究研究了各种预处理技术、特征提取方法和降维方法，以有效处理超宽带雷达数据。通过使用两脉冲消除器和离散小波变换提高了数据质量。通过创建灰度共生矩阵并随后提取统计特征来生成混合特征数据集。主成分分析用于降维，预测概率作为特征用于分类优化。本研究采用 k 折交叉验证来训练和评估机器学习分类器、决策树、随机森林、支持向量机、逻辑回归、多层感知器、K 最近邻和额外树分类器。多层感知器表现出卓越的性能，精度达到 0.936。支持向量机和 k 最近邻分类器紧随其后，两者的准确度均为 0.934。这项研究至关重要，因为它能够为多个领域的关键问题提供解决方案。研究结果表明，利用超宽带雷达数据进行基于步态的性别分类在生物识别、监控和医疗保健等多个领域都有希望。本研究对依赖步态模式的性别分类系统的进步做出了宝贵的贡献。