Human motion detection technology holds significant potential in medicine, health care, and physical exercise. This study introduces a novel approach to human activity recognition (HAR) using convolutional neural networks (CNNs) designed for individual sensor types to enhance the accuracy and address the challenge of diverse data shapes from accelerometers, gyroscopes, and barometers. Specific CNN models are constructed for each sensor type, enabling them to capture the characteristics of their respective sensors.
View Article and Find Full Text PDFBackground: This study was designed to evaluate the care of hypertensive patients in daily clinical practice in public and private centers in all Tunisian regions.
Objective: This study will provide us an overview of hypertension (HTN) management in Tunisia and the degree of adherence of practitioners to international recommendations.
Methods: This is a national observational cross-sectional multicenter study that will include patients older than 18 years with HTN for a duration of 4 weeks, managed in the public sector from primary and secondary care centers as well as patients managed in the private sector.
Background: Coronary artery diseases remain the leading cause of death in the world. The management of this condition has improved remarkably in the recent years owing to the development of new technical tools and multicentric registries.
Objective: The aim of this study is to investigate the in-hospital and 1-year clinical outcomes of patients treated with percutaneous coronary intervention (PCI) in Tunisia.
Background: Hypertension is the leading cause of morbi-mortality in our country. Thus, we conducted this national survey on hypertension to analyze the profile of the Tunisian hypertensive patient and to assess the level of blood pressure control.
Methods: Nature HTN is an observational multicentric survey, including hypertensive individuals and consulting their doctors during the period of the study.