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 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 …

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 …

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 …

The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Mobile sensing and computing applications usually require time-series inputs from sensors,
such as accelerometers, gyroscopes, and magnetometers. Some applications, such as …