João Paulo Cunha

Abstract
How we perceive and experience the world around us is inherently multisensory. Most of the Virtual Reality (VR) literature is based on the senses of sight and hearing. However, there is a lot of potential for integrating additional stimuli into Virtual Environments (VEs), especially in a training context. Identifying the relevant stimuli for obtaining a virtual experience that is perceptually equivalent to a real experience will lead users to behave the same across environments, which adds substantial value for several training areas, such as firefighters. In this article, we present an experiment aiming to assess the impact of different sensory stimuli on stress, fatigue, cybersickness, Presence and knowledge transfer of users during a firefighter training VE. The results suggested that the stimulus that significantly impacted the user's response was wearing a firefighter's uniform and combining all sensory stimuli under study: heat, weight, uniform, and mask. The results also showed that the VE did not induce cybersickness and that it was successful in the task of transferring knowledge.

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
Diagnosis of health disorders relies heavily on detecting biological data and accurately observing pathological changes. A significant challenge lies in detecting targeted biological signals and developing reliable sensing technology for clinically relevant results. The combination of data analytics with the sensing abilities of Optical Fiber Tweezers (OFT) provides a high-capability, multifunctional biosensing approach for biophotonic tools. In this work, we introduced phase as a new domain to obtain light patterns in OFT back-scattering signals. By applying a multivariate data analysis procedure, we extract phase spectral information for discriminating micro and nano (bio)particles. A newly proposed method—Hilbert Phase Slope—presented high suitability for differentiation problems, providing features able to discriminate with statistical significance two optically trapped human tumoral cells (MKN45 gastric cell line) and two classes of non-trapped cancer-derived extracellular nanovesicles – an important outcome in view of the current challenges of label-free bio-detection for multifunctional single-molecule analytic tools. © The Author(s) 2024.

Abstract
Direct blood pressure (BP) measurements require cuff compression, which not only is time-consuming but also inconvenient for frequent monitoring. This study addresses the challenge of continuous BP estimation (both Systolic (SBP) and Diastolic (DBP)) during exercise in a cuff-less manner, utilizing photoplethysmography (PPG) signals acquired by low-cost wearables. Leveraging Reflective Pulse-wave Transit Time (R-PTT), state-of-the-art algorithms were put to the test in two datasets (total subjects = 18). DATASET1 contains PPG signal and BP measurements of subjects in resting state, while DATASET2 comprises data of subjects in resting state and during exercise. The results reveal competitive performance, with Mean Absolute Error (MAE) of the estimation algorithm for DATASET1 being SBP=7.9 mmHg and DBP=5.2 mmHg and SBP=14.4 mmHg and DBP=7.7 mmHg for DATASET2. DATASET1 consistently outperforms DATASET2, affirming the algorithm's efficacy during resting states and that estimation during physical activity introduces challenges, requiring further refinement and research for real-world applications. In conclusion, this research unveils a viable solution for continuous cuff-less BP monitoring, while emphasizing the need for refinement and validation to enhance its clinical applicability and accessibility.