Muscle fatigue impacts performance in sports, rehabilitation, and daily activities, with surface electromyography (sEMG) widely used for monitoring. In this study, we developed a wearable sEMG device and conducted experiments to create a dataset for fatigue analysis. The sEMG signals were analyzed through a multi-domain feature extraction pipeline, incorporating time-domain (e.g., RMS, ARV), frequency-domain (e.g., MNF), and hybrid-domain metrics (e.g., MNF/ARV ratio, Instantaneous Mean Amplitude Difference), to identify physiologically meaningful indicators of fatigue. To ensure inter-subject comparability, we applied a dynamic standardization strategy that normalized each feature based on the RMS value of the first active segment, establishing a consistent baseline across participants. Using these standardized features, we explored several fatigue index construction methods—such as weighted sums, t-SNE, and Principal Component Analysis (PCA)—to capture fatigue progression effectively. We then trained and evaluated multiple machine learning models such as LR, SVR, RF, GBM, LSTM, CNN, and kNN to predict fatigue levels, selecting the most effective approach for real-time monitoring. Integrated into a wireless BLE-enabled sensor platform, the system offers seamless body placement, mobility, and efficient data transmission. An initial calibration phase ensures adaptation to individual muscle profiles, enhancing accuracy. By balancing on-device processing with efficient wireless communication, this platform delivers scalable, real-time fatigue monitoring across diverse applications.

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site.

Muscle fatigue impacts performance in sports, rehabilitation, and daily activities, with surface electromyography (sEMG) widely used for monitoring. In this study, we developed a wearable sEMG device and conducted experiments to create a dataset for fatigue analysis. We analyzed sEMG signals by evaluating time, frequency, and combined-domain metrics to identify reliable fatigue indicators. To ensure consistency across data sources, we applied a standardization process, calibrating signal-derived metrics to a common scale dynamically. Using these standardized metrics, we explored different methods to derive a fatigue index, through feature extraction and dimensionality reduction, assessing their effectiveness in capturing fatigue progression. We then trained and evaluated multiple machine learning models to predict fatigue levels, selecting the most effective approach for real-time monitoring. Integrated into a wireless BLE-enabled sensor platform, the system offers seamless body placement, mobility, and efficient data transmission. An initial calibration phase ensures adaptation to individual muscle profiles, enhancing accuracy. By balancing on-device processing with efficient wireless communication, this platform delivers scalable, real-time fatigue monitoring across diverse applications.

Resilience is defined as the capacity of a system to cope with a hazardous event or disturbance, responding or reorganizing in ways that maintain its essential function, identity, and structure, while also maintaining the capacity for adaptation, learning and transformation. A resilient health system is one that is capable to anticipate, respond to, cope with, recover from and adapt to system-related shocks and stress, so as to bring sustained improvements in population health, despite the unstable circumstances. Nowadays, the emergency department (ED) of hospitals faces growing demands, rising acuteness and longer waiting times. An efficient, accurate and resilient triage system to improve the operation of the ED becomes crucial. In this work, a resilient system for automatic priority, sorting and monitoring of medical events -triage system- is developed, so that the patients in the ED are treated according to the severity of their condition and not by the order of attendance utilizing a Fuzzy Inference System (FIS) that aggregates, processes patients’ vital signs as well as determines their Health Status (HS), according to which the ED staff performs the appropriate classification.