Large-Scale Indoor Camera Positioning Using Fiducial Markers.

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions.

Fiducial Objects: Custom Design and Evaluation.

Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as single markers.

UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises.

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress.

sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints.

Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g.

Validation of a new objective index to measure spinal mobility: the University of Cordoba Ankylosing Spondylitis Metrology Index (UCOASMI).

Spinal mobility measures are subject to high variability and subjectivity. Automated motion capture allows an objective and quantitative measure of mobility with high levels of precision. To validate the University of Cordoba Ankylosing Spondylitis Metrology Index (UCOASMI), an index measure of spinal mobility, based on automated motion capture, validation studies included the following: (1) validity, tested by correlation--Pearson's r--between the UCOASMI and the mobility index Bath Ankylosing Spondylitis Metrology Index (BASMI), and a measure of structural damage, the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS); (2) reliability, with internal consistency tested by Cronbach's alpha, test-retest by intraclass correlation coefficient (ICC) after 2 weeks, and error measurement, by variation coefficient (VC) and smallest detectable difference (SDD); and (3) responsiveness, by effect size (ES) in a clinical trial of anti-TNF.

Assessment of spinal mobility in ankylosing spondylitis using a video-based motion capture system.

This paper describes the use of a video-based motion capture system to assess spinal mobility in patients with ankylosing spondylitis (AS). The aim of the study is to assess reliability of the system comparing it with conventional metrology in order to define and analyze new measurements that reflect better spinal mobility. A motion capture system (UCOTrack) was used to measure spinal mobility in forty AS patients and twenty healthy subjects with a marker set defining 33 3D measurements, some already being used in conventional metrology.