Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments.

Non-invasive blood glucose monitoring technology in diabetes management: review.

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress.

Methylcellulose/chitosan nanofiber-based composites doped with lactoferrin-loaded Ag-MOF nanoparticles for the preservation of fresh apple.

Increasing demand for high-quality fresh fruits and vegetables has led to the development of innovative active packaging materials that exhibit controlled release of antimicrobial/antioxidant agents. In this study, composite biopolymer films consisting of methylcellulose (MC) and chitosan nanofibers (ChNF) were fabricated, which contained lactoferrin (LAC)-loaded silver-metal organic framework (Ag-MOF) nanoparticles. The results indicated that the nanoparticles were uniformly distributed throughout the biopolymer films, which led to improvements in tensile strength (56.

Time-domain heart rate variability features for automatic congestive heart failure prediction.

Aims: Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure.