In artificial intelligence applications, advanced computational models, such as deep
learning, are employed to achieve high accuracy, often requiring the execution of numerous …

Lacking a large-scale dataset is the major obstacle limiting sign language recognition (SLR)
to work well in the real world, because of the huge collection and annotation cost of sign …

Sign language recognition (SLR) tasks are typically performed on a large number of
continuous frames of sign language videos, making it challenging to utilize information …

Recent sign language skeletal-based feature models (SLSm) consist of various distracting
coordinates that lead to complex deep-learning modeling. However, SLSm is not purely a …

The application of tiny machine learning (TinyML) in human-computer interaction is
revolutionizing gesture recognition technologies. However, there remains a significant gap …

While recognition of sign language alphabets and isolated words has matured in recent
years, recognition of sign language sentences, or continuous signing, is still a research topic …

Lack of an effective early sign language learning framework for a hard-of-hearing population
can have traumatic consequences, causing social isolation and unfair treatment in …

Recently, eye-writing has been used as a novel language communication method, in which
the paths of eye movement are detected for character recognition. However, instability of the …

In Awake Neurosurgery (AN), the assessment of the patient's capabilities is of paramount
importance to minimize the risk of post-operative cognitive, language, and motor deficits. To …

Sign language is the communication tool for deaf and hard-of-hearing (DHH) communities
all around the world. But it is still difficult to establish proper communication between hearing …