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 …

Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is
increasing by 2% annually. Despite the significant burden that CHF poses and despite the …

Abstract The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented
a unique opportunity to the activity-recognition community to test their approaches on a …

Sensor-based human activity recognition (HAR) has been an active research area for many
years, resulting in practical applications in smart environments, assisted living, fitness …

Learning general-purpose representations from multisensor data produced by the
omnipresent sensing systems (or IoT in general) has numerous applications in diverse use …

Supervised training of human activity recognition (HAR) systems based on body-worn
inertial measurement units (IMUs) is often constrained by the typically rather small amounts …

The aim of this work is to develop and validate a miscellaneous electric loads (MEL)
predictive model that does not require occupant-wise or building-wise model training nor …

Transfer Learning is becoming increasingly important to the Human Activity Recognition
community, as it enables algorithms to reuse what has already been learned from models. It …

The use of supervised learning for Human Activity Recognition (HAR) on mobile devices
leads to strong classification performances. Such an approach, however, requires large …