The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

El presente trabajo muestra la incidencia de los tornados más representativos registrados desde el 1993 en el departamento del Atlántico ubicado al norte de Colombia que ha dejado grandes pérdidas materiales y económicas. Ante ello, se propone el diseño de un prototipo de sistema de monitorización de variables meteorológicas asociadas a la aparición de tornados en zonas donde se han presentado estos fenómenos atmosféricos para que los valores almacenados sirvan para su estudio y posterior predicción. Se emplea una red de sensores inalámbricos compuesta por cuatro nodos y un sumidero usando la plataforma Waspmote y módulos Xbee-PRO ZB (S2) para adquisición de datos y transmisión de la información. Se muestra la ubicación los nodos y la estabilidad de los datos obtenidos por los diferentes sensores del sistema.

The Human Health and Activity Laboratory (H²Al) is a new research facility at Luleå University of Technology implemented during 2018 as a smart home environment in an educational training apartment for nurses and therapists at the Luleå campus. This paper presents the design and implementation of the lab together with a discussion on potential impact. The aim is to identify and overcome economical, technical and social barriers to achieve an envisioned good and equal health and welfare within and from home environments. The lab is equipped with multiple sensor and actuator systems in the environment, worn by persons and based on digital information. The systems will allow for advanced capture, filtering, analysis and visualization of research data such as A/V, EEG, ECG, EMG, GSR, respiration and location while being able to detect falls, sleep apnea and other critical health and wellbeing issues. The resulting studies will be aimed towards supporting and equipping future home environments and care facilities, spanning from temporary care to primary care at hospitals, with technologies for activity and critical health and wellness issue detection. The work will be conducted at an International level and within a European context, based on a collaboration with other smart labs, such that experiments can be replicated at multiple sites. This paper presents some initial lessons learnt including design, setup and configuration for comparison of sensor placements and configurations as well as analytical methods.

Security systems are vital for safeguarding lives, property, and valuable information while ensuring community safety and peace of mind. In healthcare service delivery, the Internet of Things (IoT) has revolutionized operations by connecting billions of devices that automate tasks and generate massive data flows. This expansion has given rise to the Internet of Health Things (IoHT), also referred to as the Internet of Medical Things (IoMT), where smart medical devices are integrated into telehealth platforms. These systems allow healthcare professionals to access real-time patient data, thereby improving diagnosis, treatment, and overall patient care. However, the rapid proliferation of IoMT introduces significant security and privacy challenges due to the vast and complex ecosystem of connected devices. This paper presents a Systematic Literature Review (SLR) focused on addressing IoMT security and privacy issues using machine learning (ML) and advanced decision-making techniques. Specifically, the study leverages probabilistic uncertain linguistic term sets (PULTS) and q-rung orthopair fuzzy sets (q-ROFS) to handle ambiguity and uncertainty in IoMT security. To enhance decision-making reliability, the proposed framework integrates the Analytic Hierarchy Process (AHP) for criterion weighting with the Comprehensive Distance-Based Assessment (COBRA) for ranking alternatives. The combined PULq-ROF-AHP-COBRA model ensures stable priority assignment and balanced evaluation of security mechanisms.A numerical case study further demonstrates the practicality of this method, highlighting its ability to strengthen decision precision and improve security in healthcare IoMT environments.