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 (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

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 …

With the widespread application of mobile edge computing (MEC), MEC is serving as a
bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also …

Recognizing human activities and monitoring population behavior are fundamental needs of
our society. Population security, crowd surveillance, healthcare support and living …

In this paper, we present a deep learning framework “Rehab-Net” for effectively classifying
three upper limb movements of the human arm, involving extension, flexion, and rotation of …

In wearable-sensor-based activity recognition, it is often assumed that the training and test
samples follow the same data distribution. This assumption neglects practical scenarios …

Human activity recognition (HAR) is a line of research whose goal is to design and develop
automatic techniques for recognizing activities of daily living (ADLs) using signals from …

In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …

Abstract In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised domain …