Digital human modeling and gait analysis are essential for improving hip replacement surgery (HRS). In this study, Convolution Neural Networks (CNN) are used as a machine learning method to extract the most accurate stick-model from videos captured on a simple camera to represent gait and body components. We developed and tested multiple approaches to create an equitable skeleton model from an image. This process consists of two main parts: defining the joint locations using a CNN network in different architectures, and defining the connections into the final skeletons. A CNN has been trained, validated, and tested using the OpenPose software, which combines two different networks that have been tested on three data-sets for learning and evaluation. The results were satisfactory, but MobileNetV1 was evaluated for optimization of OpenPose computations and definitions. Several hyper-parameters were investigated to provide better representations. As a result of utilizing OpenPose methodology in conjunction with heavily optimized network design and post-processing code, and implementing MobileNet, the proposed solution has provided improved accuracy ratios.

(2023). Human Joint Profile Extraction using Deep Learning Approaches [journal article - articolo]. In COMPUTER-AIDED DESIGN AND APPLICATIONS. Retrieved from https://hdl.handle.net/10446/232391

Human Joint Profile Extraction using Deep Learning Approaches

Vitali, Andrea;Regazzoni, Daniele
2023-01-01

Abstract

Digital human modeling and gait analysis are essential for improving hip replacement surgery (HRS). In this study, Convolution Neural Networks (CNN) are used as a machine learning method to extract the most accurate stick-model from videos captured on a simple camera to represent gait and body components. We developed and tested multiple approaches to create an equitable skeleton model from an image. This process consists of two main parts: defining the joint locations using a CNN network in different architectures, and defining the connections into the final skeletons. A CNN has been trained, validated, and tested using the OpenPose software, which combines two different networks that have been tested on three data-sets for learning and evaluation. The results were satisfactory, but MobileNetV1 was evaluated for optimization of OpenPose computations and definitions. Several hyper-parameters were investigated to provide better representations. As a result of utilizing OpenPose methodology in conjunction with heavily optimized network design and post-processing code, and implementing MobileNet, the proposed solution has provided improved accuracy ratios.
articolo
Weisscohen, Miri; Vitali, Andrea; Regazzoni, Daniele
(2023). Human Joint Profile Extraction using Deep Learning Approaches [journal article - articolo]. In COMPUTER-AIDED DESIGN AND APPLICATIONS. Retrieved from https://hdl.handle.net/10446/232391
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/232391
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