Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

The detection and classification of pavement distress (PD) play a critical role in pavement
maintenance and rehabilitation. Research on PD automation detection and measurement …

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various
challenging machine learning problems such as image recognition. Although these methods …

Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Human action recognition systems use data collected from a wide range of sensors to
accurately identify and interpret human actions. One of the most challenging issues for …

The state of neck motion reflects cervical health. To detect the motion state of the human
neck is of important significance to healthcare intelligence. A practical neck motion detector …

Recently, automatic hand gesture recognition has gained increasing importance for two
principal reasons: the growth of the deaf and hearing-impaired population, and the …

Abstract Convolutional Neural Network based action recognition methods have achieved
significant improvements in recent years. The 3D convolution extends the 2D convolution to …