Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

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 …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

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 …

Deep learning methods are successfully used in applications pertaining to ubiquitous
computing, pervasive intelligence, health, and well-being. Specifically, the area of human …

Sensor-based human activity recognition is a fundamental research problem in ubiquitous
computing, which uses the rich sensing data from multimodal embedded sensors such as …

Machine learning and deep learning have shown great promise in mobile sensing
applications, including Human Activity Recognition. However, the performance of such …