Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …

Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …

Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …

Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …

Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …

Recent years have witnessed the success of deep learning methods in human activity
recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a …

While convolutional neural networks (CNNs) have been successfully applied to many
challenging classification applications, they typically require large datasets for training …

Objective: We explored whether use of deep learning to model temporal relations among
events in electronic health records (EHRs) would improve model performance in predicting …

Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep
learning to substitute for well-established analysis techniques that rely on hand-crafted …