The gold standard real-time core temperature (CT) monitoring methods are invasive and cost-inefficient. The application of the Kalman filter for an indirect estimation of CT has been explored in the literature for more than 10 years. This paper presents a comparative study between different state-of-the-art Extended Kalman Filter (EKF) approaches. Moreover, we proposed the addition of an extra layer to the pipeline that applies a pre-emptive mapping concept based on the physiological response of the heart rate (HR) signal, before using it as input to the EKF. The algorithm was trained and tested using two datasets (18 subjects). The best-performing approach with the novel pre-emptive mapping achieved an average Root Mean Squared Error (RMSE) of 0.34 $^{\circ }$C, while without pre-emptive mapping, it resulted in an RMSE of 0.41 $^{\circ }$C, leading to a performance improvement of 17%. Given these favorable outcomes, it is compelling to assess the efficacy of this method on a larger dataset in the future.

Epileptic seizure classification based on seizure semiology requires automated, quantitative approaches to support the diagnosis of epilepsy, which affects 1% of the world’s population. Current approaches address the problem on a seizure level, neglecting the detailed evaluation of the classification of the underlying action features, also known as Movements of Interest (MOIs), which are critical for epileptologists in determining their classifications. Moreover, it hinders objective comparison of these approaches and attribution of performance differences due to datasets, intra-dataset MOI distribution, or architecture variations. Objective evaluation of action recognition techniques is crucial, with MOIs serving as foundational elements of semiology for clinical in-bed applications to facilitate epileptic seizure classification. However, until now, there were no MOI datasets available nor benchmarks comparing different action recognition approaches for this clinical problem. Therefore, as a pilot, we introduced a novel, simulated seizure semiology dataset carried out by 8 experienced epileptologists in an EMU bed, consisting of 7 MOI classes. We compare several computer vision methods for MOI classification, two image-based (I3D and Uniformerv2), and two skeleton-based (ST-GCN++ and PoseC3D) action recognition approaches. This study emphasizes the advantages of a 2-stage skeleton-based action recognition approach in a transfer learning setting (4 classes) and the multi-scale challenge of MOI classification (7 classes), advocating for the integration of skeleton-based methods with hand gesture recognition technologies in the future. The study’s controlled MOI simulation dataset provides us with the opportunity to advance the development of automated epileptic seizure classification systems, paving the way for enhancing their performance and having the potential to contribute to improved patient care.

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.

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.

Training firefighters using Virtual Reality (VR) technology brings several benefits over traditional training methods including the reduction of costs and risks. The ability of causing the same level of stress as a real situation so that firefighters can learn how to deal with stress was investigated. An experiment aiming to study the influence that additional stimuli (heat, weight, smell and using personal protective equipment-PPE) have on user's stress level while performing a Virtual Environment (VE) designed to train firefighters was developed. Participants' stress and Heart Rate Variability (HRV) were obtained from electrocardiograms recorded during the experiment. The results suggest that wearing the PPE has the largest impact on user's stress level. The results also showed that HRV was able to evidence differences between two phases of the experiment, which suggests that it can be used to monitor users' quantified reaction to VEs.

Epilepsy affects approximately 1% of the world's population. Semi-ology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to create automatic and semiautomatic methods for seizure detection and classification to better support patient monitoring management and diagnostic decisions. One of the current promising approaches are the marker-less computer-vision techniques. In this paper an end-to-end deep learning approach is proposed for binary classification of Frontal vs. Temporal Lobe Epilepsies based solely on seizure videos. The system utilizes infrared (IR) videos of the seizures as it is used 24/7 in hospitals' epilepsy monitoring units. The architecture employs transfer learning from large object detection "static" and human action recognition "dynamic" datasets such as ImageNet and Kinectics-400, to extract and classify the clinically known spatiotemporal features of seizures. The developed classification architecture achieves a 5-fold cross-validation f1-score of 0.844±0.042. This architecture has the potential to support physicians with diagnostic decisions and might be applied for online applications in epilepsy monitoring units. Furthermore, it may be jointly used in the near future with synchronized scene depth 3D information and EEG from the seizures.

Current mobile revolution is leading to an increase of wearable health devices development and consequently a growth in ambulatory monitoring area. These systems can be applied in ambulatory diseases management and diagnosis, personal health monitoring or sports performance enhancement, providing physiological and body-area ambiance data during daily normal activities. Nowadays several devices in the market have this type of technology, being one of them the VitalJacket®(VJ®), a product from Biodevices, S.A. This device is a medical certified smart t-shirt with textile embedded electronics for ambulatory monitoring of electrocardiogram (ECG), Heart Rate (HR) and Accelerometer (Acc) data that is in the market since 2008. In this work we report the result of a university-company joint R&D effort that aimed to propose a reference extension architecture - called VitalLogger - for VJ®wearable health device that enables sensors seamless “plug-in” to the company product. As a proof of concept of this novel adaptable architecture, we jointly implemented a prototype that extends VJ®technology to incorporate new sensing modules in a seamless way. Both hardware and firmware were re-designed and extended to create a reference extension architecture, enabling easy integration of different sensing modules according to the user monitoring preference/scenario. Besides the ECG module already incorporated in VJ®, a module with a Blood Oxygen Saturation (SpO2) sensor and an ambiance temperature and relative humidity sensor was developed and added to the extended prototype VitalLogger. This prototype was successfully built and tested, showing excellent results. The VitalLogger adaptable extension architecture has shown its scalability by enabling seamless “plug-in” sensing modules thus creating the possibility of swift creations of new products with different sensors combinations to adapt to different monitoring scenarios.

The experience of daily stress among bus drivers has shown to affect physical and psychological health, and can impact driving behavior and overall road safety. Although previous research consistently supports these findings, little attention has been dedicated to the design of a stress detection method able to synchronize physiological and psychological stress responses of public bus drivers in their day-to-day routine work. To overcome this limitation, we propose a mobile sensing approach to detect georeferenced stress responses and facilitate memory recall of the stressful situations. Data were collected among public bus drivers in the city of Porto, Portugal (145 h, 36 bus drivers, +2300 km), and results supported the validation of our approach among this population and allowed us to determine specific stressor categories within certain areas of the city. Furthermore, data collected throughout the city allowed us to produce a citywide “stress map” that can be used for spotting areas in need of local authority intervention. The enriching findings suggest that our system can be a promising tool to support applied occupational health interventions for public bus drivers and guide authorities' interventions to improve these aspects in “future” cities.

Between first responder professionals, firefighters registered the highest number of deaths on duty. An abnormal high proportion is associated with cardiovascular events. Our main goal is to identify fatigue/stress during daily routine activities, focusing on the cardiovascular analysis. To accomplish this purpose, ECG wave morphological alterations are analyzed. It was observed that the RR, PP and ST segment significantly differentiate the most stressful tasks from the others.

Video can be a valuable source of information for monitoring first responders during operations in the field. In this paper we propose the MonitorMe, an application that supports online video monitoring of first responders. MonitorMe allows capturing video from a personal perspective, using a smartphone camera, and sending it to a remote observer. In order to reduce battery consumption and bandwidth usage, MonitorMe modulates the video frame rate according to the user activity/speed. The latter are estimated using the smartphone built-in accelerometer. The results have shown the potential of MonitorMe as a reliable non-GPS solution, which can be used for remote online monitoring of first responders in action.

Professionals such as First Responders are frequently exposed to extreme environmental conditions, which induce stress and fatigue during extensive periods of time. In this scenario, the main issues are the quantification and evaluation of stress and fatigue, since uncontrolled levels have a profound and negative impact on human health and performance. Based on an existing wearable monitoring solution - the Vital Jacket®- we propose an individual and team monitoring mobile solution called DroidJacket. DroidJacket is based on Android mobile devices and provides data aggregation, processing, visualization and optionally relaying services. The DroidJacket design is plugin oriented, integrating analysis modules, namely an online ECG plugin for both real time pulse and arrhythmia detection.

Endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper proposes and compares two approaches (Bayesian and support vector machines) that can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal sections and corresponding transit times. According to medical specialists, this can reduce exam annotation times by up to 12% (15 min). This automatic tool has been integrated into our CapView annotation software that is currently being used by three medical institutions.

The endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper shows how support vector machines can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal transit times. According to medical specialists, this can reduce exam annotation times by up to 12%

Recent advances in miniaturization led to the development of what is now called the endoscopic capsule. This small device is swallowed by a patient and films the whole gastrointestinal tract, allowing the detection of abnormalities. Currently, a doctor typically needs up to two hours to analyze a full exam, so automation is desirable. This paper presents a methodology for measuring the potential of selected visual MPEG-7 descriptors for the task of specific medical event detection such as blood, ulcers. Experiments show that the best results are obtained by the Scalable Color and Homogenous Texture descriptors, especially if only relevant coefficients are used.