Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …

Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …

Sign language is commonly used by deaf or speech impaired people to communicate but
requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the …

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …

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

This work develops a continuous sign language (SL) recognition framework with deep
neural networks, which directly transcribes videos of SL sentences to sequences of ordered …

Sign language recognition is a challenging problem where signs are identified by
simultaneous local and global articulations of multiple sources, ie hand shape and …

Automatic detection and classification of dynamic hand gestures in real-world systems
intended for human computer interaction is challenging as: 1) there is a large diversity in …

In this paper we propose a novel recurrent neural network architecture for video-based
person re-identification. Given the video sequence of a person, features are extracted from …

Human motion recognition is one of the most important branches of human-centered
research activities. In recent years, motion recognition based on RGB-D data has attracted …