eprintid: 8760 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/87/60 datestamp: 2023-09-11 23:30:08 lastmod: 2023-09-11 23:30:10 status_changed: 2023-09-11 23:30:08 type: article metadata_visibility: show creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Saleem, Adil Ali creators_name: Raza, Muhammad Amjad creators_name: Gracia Villar, Santos creators_name: Dzul Lopez, Luis creators_name: Diez, Isabel de la Torre creators_name: Rustam, Furqan creators_name: Dudley, Sandra creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: luis.dzul@unini.edu.mx creators_id: creators_id: creators_id: title: Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: lower limb disorder; PoseNet; gait analysis; machine learning; Artificial Neural Networks abstract: A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions. date: 2023-09 publication: Diagnostics volume: 13 number: 18 pagerange: 2881 id_number: doi:10.3390/diagnostics13182881 refereed: TRUE issn: 2075-4418 official_url: http://doi.org/10.3390/diagnostics13182881 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions. metadata Siddiqui, Hafeez Ur Rehman; Saleem, Adil Ali; Raza, Muhammad Amjad; Gracia Villar, Santos; Dzul Lopez, Luis; Diez, Isabel de la Torre; Rustam, Furqan y Dudley, Sandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, luis.dzul@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence. Diagnostics, 13 (18). p. 2881. ISSN 2075-4418 document_url: http://repositorio.unic.co.ao/id/eprint/8760/1/diagnostics-13-02881.pdf