Abstract
SummaryThis article discusses the intervertebral motion present in the craniovertebral junction (CVJ) region. The CVJ region is bounded by the first three vertebras from the spinal column. It helps in bringing most of the neck motion. Intervention in this region requires surgery in which an implant is placed to stabilize the whole system. The various available implants need to undergo performance evaluation as their performance varies from region and anatomical diversity. For the Indian population, we are targeting to evaluate the performance of such an implant, testing it into a cadaver. The region of interest will be loaded as per the loading condition of an average human. Motion in these regions is evaluated using the camera. A preliminary test was done on a saw bone model of CVJ to assess the performance of segmentation methods. Multiple such ArUco markers are used to increase pose accuracy further, and the pose of the entire board of multiple tags provides us with reliable pose estimation. The absolute error ranged from a minimum of 0.1 mm to a maximum of 16 mm. At the same time, the mean and median absolute errors were 3.8961 mm and 3.35 mm. By considering the absolute lengths, the percentage error showed the following trends. The percentage error was between 3.9168% and 0.0230%.
Highlights
A high-speed transportation system has led to many fatal accidents, resulting in destabilized craniovertebral junction (CVJ)
To get a unique solution of the forward kinematics of the Stewart platform, implementing the predefined library of ArUco markers has been used for pose estimation
The various steps involved in this method are camera calibration, marker detection process, marker identification, and pose estimation
Summary
A high-speed transportation system has led to many fatal accidents, resulting in destabilized craniovertebral junction (CVJ). The image taken from the camera is compared with the predefined library marker to estimate the pose. To get a unique solution of the forward kinematics of the Stewart platform, implementing the predefined library of ArUco markers has been used for pose estimation. To get a pose estimation of the top platform of the manipulator, the ArUco method, where a single camera and a marker give the complete pose information, is used. ArUco method A fiducial marker is used for the camera-based pose estimation. The various steps involved in this method are camera calibration, marker detection process, marker identification, and pose estimation. This is a process of characterizing parameters like focal length, principal point, skew, and image distortion This is a one-time activity for each camera, and the quality of pose estimation is enhanced by adopting this correction process.
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