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primary topic: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
type: AcademicArticle
type: Article
abstract: 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.
date: 2025-09
volume: 27
is part of: Array
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Texto)
has document: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Otro)
subject: Engineering

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Texto)
type: Document
has file: 1-s2.0-S2590005625001043-main.pdf
has part: 1-s2.0-S2590005625001043-main.pdf

Array
type: Collection
issn: 25900056

Hafeez Ur Rehman Siddiqui
type: Person
given name: Hafeez Ur Rehman
family name: Siddiqui

Isabel de la Torre Díez
type: Person
given name: Isabel
family name: de la Torre Díez

Luis Alonso Dzul López
type: Person
given name: Luis Alonso
family name: Dzul López

Adil Ali Saleem
type: Person
given name: Adil Ali
family name: Saleem

Julio César Martínez Espinosa
type: Person
given name: Julio César
family name: Martínez Espinosa

Muhammad Amjad Raza
type: Person
given name: Muhammad Amjad
family name: Raza

Sandra Dudley
type: Person
given name: Sandra
family name: Dudley

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Otro)
type: Document
is version of: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Texto)
is volatile version of: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Texto)
is index codes version of: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17849 Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
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primary topic: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence

Engineering
type: Concept
preferred label: Ingeniería @es