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. Custom fiducial objects have been developed to address these limitations by attaching markers to 3D objects, enhancing visibility from multiple viewpoints and improving precision. Existing methods mainly use square markers on non-square object faces, leading to inefficient space use. This paper introduces a novel approach for creating fiducial objects with custom-shaped markers that optimize face coverage, enhancing space utilization and marker detectability at greater distances. Furthermore, we present a technique for the precise configuration estimation of these objects using multiviewpoint images. We provide the research community with our code, tutorials, and an application to facilitate the building and calibration of these objects. Our empirical analysis assesses the effectiveness of various fiducial objects for pose estimation across different conditions, such as noise levels, blur, and scale variations. The results suggest that our customized markers significantly outperform traditional square markers, marking a positive advancement in fiducial marker-based pose estimation methods.

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. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.

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., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy.

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. Patients for the different studies all had ankylosing spondylitis. Validity studies show correlation between the BASMI (r = 0.881) and the mSASSS (r = 0.780). Reliability studies show an internal consistency of Cronbach's α = 0.894, intra-observer ICC = 0.996, test-retest ICC = 0.996, and a measurement error of VC = 2.80% and SDD = 0.25 points. Responsiveness showed an ES after 24 weeks of treatment of 0.48. In all studies, the UCOASMI's performance was better than that of the BASMI. The UCOASMI is a validated index to measure spinal mobility with better metric properties than previous indices.

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. Radiographic studies were scored using the modified Stoke Ankylosing Spondylitis Spine Score index (mSASSS). Test-retest reliability studies were performed on the same day and over a two-week period. Motion capture shows very high reliability with Intraclass Correlation Coefficient values ranging from 0.89 to 0.99, low Standard Error of the Measurement (0.37-1.33 cm and 1.58°-6.54°), correlating very well with the Bath Ankylosing Spondylitis Metrology Index (BASMI) (p < 0.001) and, in some individual measures (cervical flexion, cervical lateral flexion, back inclination, shoulder-hip angle and spinal rotation), with mSASSS (p < 0.01). mSASSS also added significantly to the variance in multivariate linear regression analysis to certain measures (back inclination, cervical flexion and cervical lateral flexion). Quantitative results obtained with motion capture system using the protocol defined show to be highly reliable in patients with AS. This technique could be a useful tool for assessing the outcome of the disease and for monitoring the evolution of spinal mobility in AS patients.