Mental stress is a prevalent issue in modern society, and detecting and classifying it accurately is crucial for effective interventions and treatment plans. This study aims to compare various machine learning (ML) algorithms for detecting mental str...
Remote Patient Monitoring Systems (RPMS) are vital for tracking patients' health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmit...
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...
Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand and high-risk environments such as transportation, construction, healthcare, and manufacturing. The development of wearable technolog...
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring...
Although the field of wearable robotic exoskeletons is rapidly expanding, there are several barriers to entry that discourage many from pursuing research in this area, ultimately hindering growth. Chief among these is the lengthy and costly developme...
Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, current methods such as finger pricking and continuous glucose monitoring systems (CGMS) are invasive, ...
The integration of wearable medical devices into surgical practice has transformed the field, enabling enhanced precision, informed decision-making, and improved patient outcomes. These devices, which include biosensors and augmented reality (AR) hea...
The widespread application of electronic skin (e-skin) in human-machine interaction necessitates intelligent and information-rich systems. However, the rapid and efficient deployment of e-skin for high-precision multisensor fusion remains a critical ...
BACKGROUND: Depression is highly recurrent, and predicting relapses in a timely manner is critical. We applied machine learning to predict the worsening of depressive symptoms.
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