Increasing demand and costs for healthcare, exacerbated by ageing populations and a
great shortage of doctors, are serious concerns worldwide. Consequently, this has …

Human activity recognition (HAR) tasks have traditionally been solved using engineered
features obtained by heuristic processes. Current research suggests that deep convolutional …

Human activity recognition (HAR) is a classification task for recognizing human movements.
Methods of HAR are of great interest as they have become tools for measuring occurrences …

In this paper, we propose a novel approach for efficient training of deep neural networks in a
bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep …

Kernels in the convolutional layers of deep convolutional networks are believed to act as
feature extractors, progressively highlighting more domain-specific features in the upper …

Optimizations in logistics require recognition and analysis of human activities. The potential
of sensor-based human activity recognition (HAR) in logistics is not yet well explored …

We present an example of unobtrusive, continuous monitoring in the home for the purpose
of assessing early health changes. Sensors embedded in the environment capture behavior …

With the popularity and development of Internet of Things (IoT) technology, human activity
recognition using IoT devices such as wearable sensors can be implemented for various …

Prior detection for high risk of falls in elderly people is an essential and challenging task.
Wearable sensors have already proven as beneficial resource in monitoring daily living …

With the rapid development of sensor types and processers, most wearable activity
recognition systems tend to making use of multiple homogeneous or heterogeneous …