João Paulo Cunha

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

Abstract
Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams. The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available. Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification - this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support. The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.

Abstract

Abstract
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants' Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before - 4 after - 7, p = .003; RE: before - 4 after - 5, p = .375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.

Abstract
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IRD action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at rdm.inesctec.pt/dataset/nis-2022-004.