Abstract

Abstract
Deep brain stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective therapy in epilepsy. Poorer surgical outcomes are related to deviations of the lead from the ANT-target. The target identification relies on the visualization of anatomical structures by medical imaging, which presents some disadvantages. This study aims to research whether ANT-LFPs recorded with the Percept (TM) PC neurostimulator can be an asset in the identification of the DBS-target. For this purpose, 17 features were extracted from LFPs recorded from a single patient, who stayed at an Epilepsy Monitoring Unit for a 5-day period. Features were then integrated into two machine learning (ML)-based methodologies, according to different LFP bipolar montages: Pass1 (nonadjacent channels) and Pass2 (adjacent channels). We obtained an accuracy of 76.6% for the Pass1-classifier and 83.33% for the Pass2-classifier in distinguishing locations completely inserted in the target and completely outside. Then, both classifiers were used to predict the target percentage of all combinations, and we found that contacts 3 (left hemisphere) and 2 and 3 (right hemisphere) presented higher signatures of the ANT-target, which agreed with the medical images. This result opens a new window of opportunity for the use of LFPs in the guidance of DBS target identification.

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.

Abstract
Wearable Health Devices (WHDs) are increasingly becoming an integral part of daily life and significantly contributing to self-monitoring in healthcare. WHDs have a wide range of applications, ranging from sports to clinical settings, where the monitoring of cardiovascular health, particularly through ECG, plays a crucial role. This study introduces a unique WHD called VitalSticker, which exhibits distinctive features such as having a comfortable tiny patch form-factor to be attached to the chest, collecting multiple vital signs with medical-grade quality (ECG, respiration, temperature and actigraphy) and seamlessly sending data to a companion app. This paper encompasses a detailed description of the hardware, firmware, and case design of the WHD. A study was conducted to assess the quality of the ECG signal acquired by VitalSticker, comparing it with the signal obtained from a CE medical-grade certified ambulatory device. The results demonstrate that our VitalSticker achieves similar medicalgrade quality when compared to the reference device, surpassing its counterpart in several specifications. Furthermore, this study presents the successful implementation of an ECG baseline wander correction filter that runs on the tiny on-board wearable microcontroller without introducing any artifacts into the ECG signal, reducing the need for further processing for this outside the wearable patch.

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. © 2023 Elsevier B.V.

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.