Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …

We exploit a self-supervised deep multi-task learning framework for electrocardiogram
(ECG)-based emotion recognition. The proposed solution consists of two stages of learning …

Heart disease is one of the most common diseases in the world. The objective of this study is
to aid the diagnosis of heart disease using a hybrid classification system based on the …

Gait recognition refers to the identification of individuals based on features acquired from
their body movement during walking. Despite the recent advances in gait recognition with …

Emotion plays crucial roles in human life. Recently, emotion classification from
electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid …

Radial basis function (RBF) neural network can use linear learning algorithm to complete
the work formerly handled by nonlinear learning algorithm, and maintain the high precision …

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 …

This paper proposes a novel method for the electrocardiographic (ECG) beat classification
via deterministic learning. The dynamics of ECG beats is used as a unique feature for ECG …

Gender classification plays an important role in many applications such as security and
medical applications. Human gender can be classified using different biometric techniques …

Sleep posture analysis is widely used for clinical patient monitoring and sleep studies.
Earlier research has revealed that sleep posture highly influences symptoms of diseases …