RT info:eu-repo/semantics/article
T1 Empowering lower limb disorder identification through PoseNet and artificial intelligence
A1 Siddiqui, Hafeez Ur Rehman
A1 Saleem, Adil Ali
A1 Raza, Muhammad Amjad
A1 Gracia Villar, Santos
A1 Dzul López, Luis Alonso
A1 Torre Díez, Isabel de la
A1 Rustam, Furqan
A1 Dudley, Sandra
K1 Extremidades inferiores - Enfermedades
K1 Leg - Diseases
K1 Locomoción humana
K1 Marcha (Ejercicio)
K1 Biomedical engineering
K1 Biomechanics
K1 Clinical biochemistry
K1 Bioquímica clínica
K1 Machine learning
K1 Aprendizaje automático
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Artificial intelligence
K1 1203.17 Informática
K1 1203.04 Inteligencia Artificial
K1 32 Ciencias Médicas
K1 2406.04 Biomecánica
AB 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.
PB MDPI
SN 2075-4418
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/66789
UL https://uvadoc.uva.es/handle/10324/66789
LA eng
NO Diagnostics, 2023, Vol. 13, Nº. 18, 2881
NO Producción Científica
DS UVaDOC
RD 17-may-2024