TY  - JOUR
KW  - lower limb disorder; PoseNet; gait analysis; machine learning; Artificial Neural Networks
IS  - 18
Y1  - 2023/09//
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Saleem, Adil Ali
A1  - Raza, Muhammad Amjad
A1  - Gracia Villar, Santos
A1  - Dzul Lopez, Luis
A1  - Diez, Isabel de la Torre
A1  - Rustam, Furqan
A1  - Dudley, Sandra
TI  - Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence
N2  - 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.
UR  - http://doi.org/10.3390/diagnostics13182881
ID  - uninimx8760
SN  - 2075-4418
JF  - Diagnostics
AV  - public
VL  - 13
ER  -