["Assure" program, to improve the management of emergency situations in Ehpad].

The Assure approach (improvement of emergency care) is a project designed to improve emergency care for the 63,000 residents of homes for the dependent elderly (Ehpad) in the Ile-de-France region. By reinforcing the skills of caregivers in emergency situations and facilitating collaboration between care providers, the Assure approach is mobilizing, alongside all the Ehpad in the Ile-de-France region over a period of two years, the emergency medical assistance services, emergency physicians, mobile geriatric teams and nursing and care assistant training institutes.

Phenotype and outcomes of very early onset and early onset inflammatory bowel diseases in a Montreal pediatric cohort.

Objectives: The incidence of very-early-onset inflammatory bowel disease (VEO-IBD) and early-onset IBD (EO-IBD) is increasing. Here, we report their phenotype and outcomes in a Montreal pediatric cohort.

Methods: We analyzed data from patients diagnosed with IBD between January 2014 and December 2018 from the CHU Sainte-Justine.

Convolutional Neural Network Bootstrapped by Dynamic Segmentation and Stigmergy-Based Encoding for Real-Time Human Activity Recognition in Smart Homes.

Recently, deep learning (DL) approaches have been extensively employed to recognize human activities in smart buildings, which greatly broaden the scope of applications in this field. Convolutional neural networks (CNN), well known for feature extraction and activity classification, have been applied for estimating human activities. However, most CNN-based techniques usually focus on divided sequences associated to activities, since many real-world employments require information about human activities in real time.

Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context.

Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data.

A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning.

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents.