Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

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....

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-09, Vol.13 (18), p.2881
Main Authors: Siddiqui, Hafeez Ur Rehman, Saleem, Adil Ali, Raza, Muhammad Amjad, Villar, Santos Gracia, Lopez, Luis Alonso Dzul, Diez, Isabel de la Torre, Rustam, Furqan, Dudley, Sandra
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Ankle
Arthritis
Artificial intelligence
Artificial Neural Networks
Asymmetry
Automation
Cameras
Classification
Data collection
Data mining
Deep learning
Discriminant analysis
Gait
gait analysis
Knee
Literature reviews
lower limb disorder
Machine learning
Mechanics
Osteoarthritis
Patients
PoseNet
Sensors
Support vector machines
Walking
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13182881