TY  - JOUR
AV  - public
JF  - Array
N2  - Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.
KW  - Gait; Ultra-wide band radar; Gender classification; Spectral features; Feed forward artificial neural network; Ridge classifier; Hist gradient boosting
VL  - 27
SN  - 25900056
UR  - http://doi.org/10.1016/j.array.2025.100477
A1  - Saleem, Adil Ali
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Raza, Muhammad Amjad
A1  - Dudley, Sandra
A1  - Martínez Espinosa, Julio César
A1  - Dzul López, Luis Alonso
A1  - de la Torre Díez, Isabel
TI  - Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
ID  - uneatlantico17849
Y1  - 2025/09//
ER  -