Biomechanical Model for Dynamic Analysis of the Human Gait

Martins, Jordana Simões Ribeiro; Sabino, George Schayer; Nascimento, Diego Henrique Antunes; Machado, Gabriel M. C.; Vimieiro, Claysson Bruno Santos

doi:10.1007/978-3-030-43195-2_30

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Furthermore, the present study showcases comparable performance to previous studies that employed 3D skeletal data and foot pressure measurements [ 39 ]. Moreover, it exhibits favorable comparisons to studies that utilized radiographic imaging and gait analysis data, which reported area under the curve (AUC) values ranging from 0.82 to 0.97 [ 18 ]. In general, the study presents encouraging results, indicating the potential of utilizing PoseNet features extracted from video data as a viable method in classifying lower limb disorders.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Gait analysis is employed in a wide variety of domains, including medical diagnostics [ 13 , 14 , 15 ], osteopathic medicine [ 16 , 17 ], comparative bio-mechanics [ 18 , 19 , 20 ], and sports-related bio-mechanics [ 21 , 22 , 23 ]. The application of gait analysis has exhibited considerable promise in the identification and assessment of lower limb disorders [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Results and Discussionmentioning

confidence: 99%

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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“…Kinematics is a very important part, because the results of the dynamics depend on it. A lot of the models on the market today focus on the lower body and mostly gait [15][16][17]. BTS Bioengineering motion capture system has widely developed the Helen Hayes Protocol which allows to perform detailed gait analysis [18].…”

Section: Introductionmentioning

confidence: 99%

Skeletal Modeling in Rhino Grasshopper—A Confirmed Kinematic Model

Senvaitis,

Daunoravičienė

2023

Machines

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As musculoskeletal modeling improves, the possibilities of calculating more diverse parameters or performing specific motion analyses increase. However, customization might require a different approach that is not offered by the original software or it requires complex knowledge. Patient lift motion was analyzed in Plug-in-Gait (PiG) marker-set-based kinematic model in Rhino Grasshopper for the range of motion calculation of arms. The model was compared with the biomechanics of body (BoB) 10.5 software kinematic model. For the analyzed model, RMSE evaluated as a percentage of the amplitude varied from 9.17% to 32.44%. The data showed actively accurate results except for a few values that were defined as moderately accurate. All data sets showed strong correlation with the reference model. The tested model was confirmed, since it showed significant data correlation with relative accurate values and was evaluated as suitable for further development and analysis before being put to practical use.

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Biomechanical Model for Dynamic Analysis of the Human Gait

Cited by 2 publications

References 9 publications

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Skeletal Modeling in Rhino Grasshopper—A Confirmed Kinematic Model

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