Victor-Ștefan Constantin

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments.

The aging population requires cutting-edge approaches to geriatric care, with digital health technologies playing a crucial part in meeting the challenging demands of healthcare. Current approaches frequently fall short of the goal of providing comprehensive, real-time monitoring and merging contextually complex information for use in the treatment of patients. This paper addresses these limitations by integrating the innovative approaches within the RO-SmartAgeing system and the NeuroPredict platform to boost geriatric-care outcomes. It emphasizes the multifaceted design and development processes of these digital health solutions, emphasizing a multidisciplinary approach and a meticulous choice of decision-making tools. This paper presents the inclusion of decision-making tools, namely the Medical Blackbox and Gaitband, into the RO-SmartAgeing system and the NeuroPredict platform; these tools have been developed for the purpose of gathering complex physiological data and allow for in-depth evaluations of gait patterns and vital health parameters in elderly individuals. The present research emphasizes major breakthroughs in sensing technology and decision-making capabilities, illustrating the manner in which these tools enhance patient outcomes by providing timely, data-driven insights. The results demonstrate that these tailored decision-making tools significantly improve patient outcomes, underscoring the need for such ongoing improvements able to address digital health solutions tailored to the dynamic demands of an increasingly aging population.