Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

Human Activity Recognition (HAR) is a field that infers human activities from raw time-series
signals acquired through embedded sensors of smartphones and wearable devices. It has …

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

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 …

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 …

In the recent past, recognition of human locomotion activities has become a growing
research area. Health monitoring, detection of a crowd's behavior and indoor-localization …

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

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

The ubiquitous availability of wearable sensing devices has rendered large scale collection
of movement data a straightforward endeavor. Yet, annotation of these data remains a …

Human activity recognition (HAR) is a process of identifying the daily living activities of an
individual using a set of sensors and appropriate learning algorithms. Most of the works on …