In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.

The gateway reflex is a mechanism by which neural inputs regulate chemokine expression at endothelial cell barriers, thereby establishing gateways for the invasion of autoreactive T cells into barrier-protected tissues. In this study, we hypothesized that rod photoreceptor dysfunction causes remodeling of retinal neural activity, which influences the blood–retinal barrier and the development of retinal inflammation. We evaluated this hypothesis using Gnat1^rd17 mice, a model of night blindness with late-onset rod-cone dystrophy, and experimental autoimmune uveoretinitis (EAU). Retinal remodeling and its effect on EAU development were investigated by transcriptome profiling, target identification, and functional validation. We showed that Gnat1^rd17 mice primarily underwent alterations in their retinal dopaminergic system, triggering the development of an exacerbated EAU, which was counteracted by dopamine replacement with L-DOPA administered either systemically or locally. Remarkably, dopamine acted on retinal endothelial cells to inhibit NF-κB and STAT3 activity and the expression of downstream target genes such as chemokines involved in T cell recruitment. These results suggest that rod-mediated dopamine release functions in a gateway reflex manner in the homeostatic control of immune cell entry into the retina, and the loss of retinal dopaminergic activity in conditions associated with rod dysfunction increases the susceptibility to autoimmune uveitis.

Monitoring cognitive load is critical in diverse, demanding environments, yet conventional assessment methods face limitations in real-time applicability. While machine learning approaches using physiological signals show promise, they often require long data segments, exhibit high computational complexity, or neglect underlying causal dynamics. This paper proposes an efficient framework for cognitive load decoding using causal spatiotemporal patterns derived from multimodal peripheral biosignals. We introduce a novel feature engineering pipeline that transforms short signal segments into image-like representations capturing temporal dynamics via Gramian Angular Difference Fields and Motif Difference Fields, alongside causal interdependencies assessed using forward-backward copula Granger causality networks. These fused multimodal features are classified using a lightweight capsule neural network employing a self-attention routing mechanism. To evaluate the proposed solution, we conducted experiments on two widely used benchmark datasets, WESAD and CLAS. Our model achieved up to 94% accuracy using only 5-second signal segments, and maintained robust performance (84% accuracy) even with 1-second windows, a configuration rarely addressed in prior research. The proposed architecture includes only 323K trainable parameters, offering a favorable balance between model complexity and classification performance. The results confirm the framework's potential for computationally efficient, real-time cognitive load assessment suitable for resource-constrained environments and biofeedback applications.