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Further Developments
Published in Junichi Takeno, Self-Aware Robots, 2022
The timing was right, and this head robot was capable of almost the same movement as a human head. The robot was made of aluminum alloy and had almost the same shape as a human skull and cervical vertebrae. All parts of the robot were handmade by the students and the author (Fig. 11.27). We made a wax model of the parts, and then a lost-wax process was used to cast the aluminum alloy robot. We outsourced the lost-wax process to an expert in aluminum casting. The robot neck (Fig. 11.27, (1)) was made thicker than a human neck and it was about five times thicker. A human neck has thick muscles that support the weight of the skull, but this robot head uses steel cables in place of the muscles. For this reason, the neck itself is thicker to support the weight of the skull. The structure of the neck also imitates human morphology.
Designing for Head and Neck Anatomy
Published in Karen L. LaBat, Karen S. Ryan, Human Body, 2019
Flexion-extension is the forward and backward bending motion of the head and neck in the midsagittal plane. Look at the flexion-extension ROM (Figure 3.15, center left), and the muscles which control the motion (Figure 3.15 upper, center, and lower). The front to back “yes” nodding motion of the head (Figure 3.15, center right) accounts for about 20 degrees of movement. It occurs in the sub-occipital segment of the spine (between the skull and the atlas, C1, plus between C1 and C2). The R and L sternocleidomastoid and several small deep muscles originating on the upper cervical vertebrae and inserting on the skull create the nodding motion of the head on the neck. At each level below that segment (from C2-C3 to C7-T1) there are another approximate 10–15 degrees of motion, for 110 additional degrees of mobility. The total flexion-extension ROM from maximum forward motion to maximum backward motion is approximately 130 degrees.
Automatic vertebra detection in X-ray images
Published in João Manuel, R. S. Tavares, R. M. Natal Jorge, Computational Modelling of Objects Represented in Images, 2018
C. Moura Daniel, Miguel V. Correia, Jorge G. Barbosa, Ana M. Reis, Manuel Laranjeira, Gomes Eusébio
Having isolated the spine, the next task is to divide it in vertebrae. If we look closely, we can see that vertebrae are usually bright and the disks that separate them have lower intensity. Based on this observation, we built an algorithm that detects discontinuities along the spine and tries to figure out if they may indicate the presence of a disk separating vertebrae. However, vertebrae intensity vary a lot: cervical vertebrae usually have low intensity and lumbar vertebrae usually present very high intensity. This makes it difficult to classify regions as vertebrae because we cannot define pattern levels of intensity. The intensity of a vertebra depends of its position and of the image acquisition equipment. For tackling this problem our algorithm uses a progressive thresholding approach. The algorithm starts by counting the number of pixels per row at a very low threshold. Then, the threshold value is incremented at a slow rate and the counting process is repeated. Figure 5 illustrates in the right side the result of applying this technique, although we only included some threshold values for demonstration proposes. As we may observe, with low threshold levels we are able to isolate vertebrae with low intensity (typically at the cervical) and with higher threshold levels we accomplish to detect vertebrae with higher intensity.
Analysis of stress and stabilization in adolescent with osteoporotic idiopathic scoliosis: finite element method
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Qiaolin Zhang, Yan Zhang, Teo. Ee Chon, Julien S. Baker, Yaodong Gu
The images were segmented using Mimics 20.0 (Materialise, Leuven, Belgium) to obtain the boundaries of the skeleton. The uneven surfaces caused by stacking of the medical images were processed using Geomagic Studio (2013) (Geomagic, Inc., Research Triangle Park, NC, United States). Each surface component was then imported into Solidworks 2020 (SolidWorks Corporation, MA, United States) individually to form solid parts. The annulus fibrosis, nucleus pulposus, endplate, and articular cartilage were established in Solidworks 2020 according to the anatomical characteristics of the lumbar spine. The thickness of the cortical bone of the vertebral body ranges from 0.18 mm to 0.6 mm (Ritzel et al. 1997). The average thickness of cortical bone is about 0.2 mm. The width of the lumbar vertebrae is larger than that of the thoracic or cervical vertebrae. The thickness of cortical bone does not depend on individual sex but decreases with age. In this study, the thickness of cortical bone is 0.4 mm and the thickness of endplate is 0.5 mm (Gómez et al. 2017).
Automatic identification of three-dimensional morphometric features of vertebrae
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Junhua Zhang, Bo Li, Hongjian Li, Shuai Zhang, Wentao Yu
The lumbar and thoracic vertebrae have similar morphometric features, which differ from those of the cervical vertebrae. In this study, the lumbar and thoracic vertebrae were considered. For each vertebra to be measured, a 3D model was required as input to the system. The system measured the most used morphometric features, including the Spinous Process Length (spl), Spinous Process Angle (spa), Superior Vertebral Body Length and Width (svbl and svbw), Inferior Vertebral Body Length and Width (ivbl and ivbw), Left and Right lateral Vertebral Body Height (lvbh and rvbh), Anterior and Posterior lateral Vertebral Body Height (avbh and pvbh), and Vertebral Canal Length and Breadth (vcl and vcb), as shown in Figure 2. Detailed descriptions of the 12 features are described by Di Angelo and Di Stefano (2015).
From the field of play to the laboratory: Recreating the demands of competition with augmented reality simulated sport
Published in Journal of Sports Sciences, 2020
Kahlee Adams, Adam Kiefer, Derek Panchuk, Adam Hunter, Ryan MacPherson, Wayne Spratford
The University of Western Australia (UWA) lower limb marker set was used, which consisted of 30 retro-reflective markers (14 mm in diameter), including four “T-bar” clusters (consisting of three retro-reflective markers) on the participant’s thighs and shanks (Besier, Sturnieks, Anderson, & Lloyd, 2003). Four of these markers were placed on the participant’s pelvic region, consisting of the right and left anterior superior iliac spine and the right and left posterior superior iliac spine. Four additional markers were also placed on the thorax, consisting of the supracostal notch, xiphoid process (sternum), seventh cervical vertebrae and the tenth thoracic vertebrae. Trajectory data for biomechanical evaluation were collected using a 14 camera Vicon Motion Analysis System (Oxford Metrics Ltd, Oxford, UK), sampling at 250 Hz. These data were synchronised with the vGRF data, measured by two 600x900mm force plates (9287BA; Kistler Instrumente, Winterthur, Switzerland) sampling at a 1000 Hz. The two force plates were embedded adjacent to each other in the floor of the laboratory, between the net and the position of the hanging ball (Figure 1), with the net positioned in line with the most distal end of the force plates. To align the participant with the global coordinate system and identify the joint axes, a “static” measurement was collected for each participant prior to commencing the testing conditions.