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.

Background/Objectives: As musculoskeletal injuries in gastroenterologists related to the performance of endoscopic procedures are on the rise, solutions and new approaches are needed to prevent these undesired outcomes. In our study, we evaluated an approach to ergonomic challenges in the form of a belt-like endoscope holder designed to redistribute the weight of the endoscope across the whole body of the practitioner. The aim of the study was to determine how the use of this holder affected the body posture of practitioners during endoscopy. Methods: We designed a special endoscopic model that emulates basic endoscopic movement and maneuvers. With the use of the MoCap camera system, we recorded experienced endoscopists exercising a standardized set of tasks with and without the holder. Results: Following video and statistical analyses, the most significant differences were observed in the position of the left arm which pointed to a more relaxed arm position. Conclusions: The ergonomic benefits of the belt holder in this model merit testing in the clinical setting to evaluate its effectiveness and prevention of musculoskeletal injuries in GI endoscopy.

Gunshot residue pattern assessment can support forensic reconstruction, particularly for shooting distance evaluation, but commonly used approaches may be time-consuming, costly, or dependent on subjective visual interpretation, which can be especially difficult on challenging substrates. At the same time, no prior study, to our knowledge, has combined flash-pulse infrared thermography with multi-task deep learning for simultaneous prediction of multiple ballistic attributes from thermographic deposition patterns on textiles. In this proof-of-concept study, we investigated whether these patterns associated with firearm discharge residues and damage on textile targets can support complementary ballistic screening under controlled laboratory conditions. Using a dataset of 312 ballistic samples, we developed a hybrid multi-task neural network to classify weapon category, firearm model, and ammunition type, while also estimating shooting distance. The final model achieved mean accuracies of 94.3% for weapon category classification, 85.1% for firearm model classification, and 92.6% for ammunition type classification, together with a mean absolute error of 7.92 cm for shooting distance estimation. The proposed approach outperformed the evaluated traditional machine-learning baselines and single-task neural-network models within this experimental setting. These findings support the feasibility of combining flash-pulse infrared thermography and multi-task deep learning as a rapid, non-destructive, and objective complementary tool for ballistic screening and shooting distance assessment in controlled conditions. However, because the study was limited to a restricted set of firearms, ammunition types, textile conditions, and laboratory-controlled samples, the method should be regarded as a preliminary screening approach rather than a confirmatory forensic identification method.