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 …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Recent researches on sensor based human activity recognition (HAR) are mostly devoted to
designing various network architectures to enhance their feature representation capacity for …

Accurate physical activity monitoring is essential to understand the impact of physical activity
on one's physical health and overall well-being. However, advances in human activity …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

The emergence of self-supervised learning in the field of wearables-based human activity
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …

A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need
for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is …

It is undeniable that mobile devices have become an inseparable part of human's daily
routines due to the persistent growth of high-quality sensor devices, powerful computational …

Feature extraction is crucial for human activity recognition (HAR) using body-worn
movement sensors. Recently, learned representations have been used successfully, offering …

Featured Application The proposed technique is an application of physical activity detection,
analyzing three challenging benchmark datasets. It can be applied in sports assistance …