<b><span style="font-family:;" "="">Aim:</span></b><span><span><span style="font-family:;" "=""> To perform a vector 3D recon...<b><span style="font-family:;" "="">Aim:</span></b><span><span><span style="font-family:;" "=""> To perform a vector 3D reconstruction of the neck skeleton from the anatomical sections of the “Korean Visible Human” for educational purposes. <b>Material and Methods: </b>The anatomical subject was a 33-year-old Korean male who died of leukemia. It measured 164 cm and weighed 55</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">kgs.</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">The anatomical cuts were made in 2010 after an MRI and a CT scan. A special saw (cryomacrotome) made it possible to make cuts on the frozen body of 0.2 mm thick or 5960 slices. Sections numbered 1500 to 2000 (500 neck sections) were used for this study. Manual contouring segmentation of each anatomical element of the anterior neck area was done using Winsurf software version 3.5 on a PC. <b>Results</b>: Our vector 3D neck model includes the following: cervical vertebrae, hyoid bone, sternum manubrium and clavicles. This vector model has been integrated into the virtual dissection table</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">Diva3d, a new educational tool used by universities and medical schools to learn anatomy. This model was also put online on the Sketchfab website and printed in 3D using an ENDER 3 printer. <b>Conclusion:</b> This original work is a remarkable educational tool for the study of the skeleton of the neck and can also serve as a 3D atlas for simulation purposes for training therapeutic gestures.</span></span></span>展开更多
This research presents a novel way of labelling human activities from the skeleton output computed from RGB-D data from vision-based motion capture systems. The activities are labelled by means of a Compound Hidden Ma...This research presents a novel way of labelling human activities from the skeleton output computed from RGB-D data from vision-based motion capture systems. The activities are labelled by means of a Compound Hidden Markov Model. The linkage of several Linear Hidden Markov Models to common states, makes a Compound Hidden Markov Model. Each separate Linear Hidden Markov Model has motion information of a human activity. The sequence of most likely states, from a sequence of observations, indicates which activities are performed by a person in an interval of time. The purpose of this research is to provide a service robot with the capability of human activity awareness, which can be used for action planning with implicit and indirect Human-Robot Interaction. The proposed Compound Hidden Markov Model, made of Linear Hidden Markov Models per activity, labels activities from unknown subjects with an average accuracy of 59.37%, which is higher than the average labelling accuracy for activities of unknown subjects of an Ergodic Hidden Markov Model (6.25%), and a Compound Hidden Markov Model with activities modelled by a single state (18.75%).展开更多
文摘<b><span style="font-family:;" "="">Aim:</span></b><span><span><span style="font-family:;" "=""> To perform a vector 3D reconstruction of the neck skeleton from the anatomical sections of the “Korean Visible Human” for educational purposes. <b>Material and Methods: </b>The anatomical subject was a 33-year-old Korean male who died of leukemia. It measured 164 cm and weighed 55</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">kgs.</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">The anatomical cuts were made in 2010 after an MRI and a CT scan. A special saw (cryomacrotome) made it possible to make cuts on the frozen body of 0.2 mm thick or 5960 slices. Sections numbered 1500 to 2000 (500 neck sections) were used for this study. Manual contouring segmentation of each anatomical element of the anterior neck area was done using Winsurf software version 3.5 on a PC. <b>Results</b>: Our vector 3D neck model includes the following: cervical vertebrae, hyoid bone, sternum manubrium and clavicles. This vector model has been integrated into the virtual dissection table</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">Diva3d, a new educational tool used by universities and medical schools to learn anatomy. This model was also put online on the Sketchfab website and printed in 3D using an ENDER 3 printer. <b>Conclusion:</b> This original work is a remarkable educational tool for the study of the skeleton of the neck and can also serve as a 3D atlas for simulation purposes for training therapeutic gestures.</span></span></span>
文摘This research presents a novel way of labelling human activities from the skeleton output computed from RGB-D data from vision-based motion capture systems. The activities are labelled by means of a Compound Hidden Markov Model. The linkage of several Linear Hidden Markov Models to common states, makes a Compound Hidden Markov Model. Each separate Linear Hidden Markov Model has motion information of a human activity. The sequence of most likely states, from a sequence of observations, indicates which activities are performed by a person in an interval of time. The purpose of this research is to provide a service robot with the capability of human activity awareness, which can be used for action planning with implicit and indirect Human-Robot Interaction. The proposed Compound Hidden Markov Model, made of Linear Hidden Markov Models per activity, labels activities from unknown subjects with an average accuracy of 59.37%, which is higher than the average labelling accuracy for activities of unknown subjects of an Ergodic Hidden Markov Model (6.25%), and a Compound Hidden Markov Model with activities modelled by a single state (18.75%).
