With the widespread proliferation of (miniaturized) sensing facilities and the massive growth
and popularity of the field of machine learning (ML) research, new frontiers in automated …

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 …

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

Human activity recognition (HAR) using wearable sensors is always a research hotspot in
ubiquitous computing scenario, in which feature learning has played a crucial role. Recent …

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 …

The lack of large-scale, labeled data sets impedes progress in developing robust and
generalized predictive models for on-body sensor-based human activity recognition (HAR) …

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 …

Deep Learning models, applied to a sensor-based Human Activity Recognition task, usually
require vast amounts of annotated time-series data to extract robust features. However …

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 …

Abstract Human Activity Recognition from body-worn sensor data poses an inherent
challenge in capturing spatial and temporal dependencies of time-series signals. In this …