Disorders of the Lower Extremity

Leggit, Jeff; Carey, Patrick; Alisangco, Jason B.

doi:10.1007/978-3-319-04414-9_118

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Lower extremity disorders have been identified as a significant factor contributing to disability and reduced quality of life on a global scale [ 1 , 2 ]. Osteoarthritis of the knee, hip, and ankle is a commonly observed disorder affecting the lower limbs [ 3 , 4 ]. These conditions, commonly resulting from trauma, degenerative diseases, or bio-mechanical abnormalities, can give rise to discomfort, a restricted range of motion, and diminished functionality [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Siddiqui,

Saleem,

Raza

et al. 2023

Diagnostics

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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.

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