Abstract
In this paper, a solution to monitor the location of humans during their activity in the agriculture sector with the aim to boost productivity and efficiency is provided. Our solution is based on map-matching methods, that are used to track the path spanned by a worker along a specific activity in an agriculture culture. Two different cultures are taken into consideration in this study olives and vines. We leverage the symmetry of the geometry of these cultures into our solution and divide the problem three-fold initially, we estimate a path of a worker along the fields, then we apply the map-matching to such path and finally, a post-processing method is applied to ensure local continuity of the sequence obtained from map-matching. The proposed methods are experimentally evaluated using synthetic and real data in the region of Mirandela, Portugal. Evaluation metrics show that results for synthetic data are robust under several sampling periods, while for real-world data, results for the vine culture are on par with synthetic, and for the olive culture performance is reduced.

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
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.

Abstract
The progressive and complex nature of Parkinson's disease (PD) may largely benefit from regular and personalised monitoring, which is beyond the current clinical practice and routinely available systems. This paper proposes a simple and effective system to address this issue by using a wearable device to quantify a key PD's motor symptom - gait impairment as a proof-of-concept for a future broader approach. In this study, 60 inertial signals were collected from the ankle in 41 PD patients during a clinical standard gait assessment exercise. Each exercise iteration was classified by a specialised neurologist based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). A signal processing and feature extraction pipeline was employed to characterise gait, followed by a statistical analysis of their ability to differentiate between the 5 levels of impairment. The results revealed that 4 of the 8 studied features exhibited high discriminatory power between different severity levels of gait impairment, with statistical significance. The distinguishing capability of these 4 extracted features - gait consistency, rotation angle, mean height and length of steps - holds great promise for the development of a gait severity quantification remote monitoring for PD patients at home or on the move, proving the concept of the usefulness of wearable devices for regular and personalised PD symptom monitoring.

Abstract
Agriculture work is physically demanding and the sector workers have a high incidence of musculoskeletal disorders. The shift to Agriculture 5.0 and the advancement of precision agriculture have involved the digitalization of this industry, but tend to marginalise the workers, though they are still essential to more thorough tasks that cannot be automated. In order to tackle the necessity to support the monitoring of agriculture workers, we developed quantification algorithms, incorporated in a mobile application, which calculate metrics based on the signals gathered by wearable sensors. Our proximity to the Douro region lead us to focus on metrics that could be more meaningful for viniculture, namely the quantification of trunk inclinations and shear cuts, very common in this production. The developed algorithms showed an error of 1.36 degrees for the calculus of inclination and 2.43 cuts for the prediction of cuts when tested with on-field data. These results suggest that the created system has the viability to be used by agricultures and give reliable feedback on their workers.