In order to exploit representations of time-series signals, such as physiological signals, it is
essential that these representations capture relevant information from the whole signal. In …

There is a growing demand for emotion recognition systems (ERS) to be adopted in
everyday life from various fields, particularly automotive, education, and social security …

Recently, wearable emotion recognition based on peripheral physiological signals has
drawn massive attention due to its less invasive nature and its applicability in real-life …

The introduction of the Internet of Things (IoT) has made several emerging applications, from
financial transactions to property access, possible through IoT-connected smart wearables …

Stress recognition is the process of identifying and assessing an individual's physiological
and psychological responses to stressors, which has significant implications for human well …

We propose cross-modal attentive connections, a new dynamic and effective technique for
multimodal representation learning from wearable data. Our solution can be integrated into …

Ubiquitous in-home health monitoring systems have become popular in recent years due to
the rise of digital health technologies and the growing demand for remote health monitoring …

In this paper, we address the problem of multimodal emotion recognition from multiple
physiological signals. We demonstrate that a Transformer-based approach is suitable for …

Classification of human emotions can play an essential role in the design and improvement
of human-machine systems. While individual biological signals such as Electrocardiogram …

Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-
Drive, which contains Electroencephalogram (EEG) signals along with other physiological …