eprintid: 17849 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/49 datestamp: 2025-09-17 23:30:07 lastmod: 2025-09-17 23:30:09 status_changed: 2025-09-17 23:30:07 type: article metadata_visibility: show creators_name: Saleem, Adil Ali creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Raza, Muhammad Amjad creators_name: Dudley, Sandra creators_name: Martínez Espinosa, Julio César creators_name: Dzul López, Luis Alonso creators_name: de la Torre Díez, Isabel creators_id: creators_id: creators_id: creators_id: creators_id: ulio.martinez@unini.edu.mx creators_id: luis.dzul@uneatlantico.es creators_id: title: Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Gait; Ultra-wide band radar; Gender classification; Spectral features; Feed forward artificial neural network; Ridge classifier; Hist gradient boosting 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 publication: Array volume: 27 pagerange: 100477 id_number: doi:10.1016/j.array.2025.100477 refereed: TRUE issn: 25900056 official_url: http://doi.org/10.1016/j.array.2025.100477 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Saleem, Adil Ali; Siddiqui, Hafeez Ur Rehman; Raza, Muhammad Amjad; Dudley, Sandra; Martínez Espinosa, Julio César; Dzul López, Luis Alonso y de la Torre Díez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ulio.martinez@unini.edu.mx, luis.dzul@uneatlantico.es, SIN ESPECIFICAR (2025) Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence. Array, 27. p. 100477. ISSN 25900056 document_url: http://repositorio.unincol.edu.co/id/eprint/17849/1/1-s2.0-S2590005625001043-main.pdf