This paper firstly introduces common wearable sensors, smart wearable devices and the key
application areas. Since multi-sensor is defined by the presence of more than one model or …

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 …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Nowadays, multi-sensor technologies are applied in many fields, eg, Health Care (HC),
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area,
especially due to the spread of electronic devices such as smartphones, smartwatches and …

The recent advances in deep transfer learning reveal that adversarial learning can be
embedded into deep networks to learn more transferable features to reduce the distribution …

The discovery of several machine learning and deep learning techniques has paved the
way to extend the reach of humans in various real-world applications. Classical machine …

Transfer learning aims to learn robust classifiers for the target domain by leveraging
knowledge from a source domain. Since the source and the target domains are usually from …

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

Wearable sensor-based human activity recognition (HAR) has emerged as a principal
research area and is utilized in a variety of applications. Recently, deep learning-based …