Sign language training for Glasgow nurses

AbstractThis article examines available (mainly anecdotal) evidence related to the experience of humor among chimpanzees and gorillas in the wild, in captivity and following systematic sign language training. Humor is defined as one form of symbolic play. Positive evidence of object permanence, cross-modal perception, deferred imitation and deception among chimpanzees and gorillas is used to document their cognitive capacity for humor. Playful teasing is proposed as the primordial form of humor among apes in the wild. This same form of humor is commonly found among signing apes, both in overt behavior and in signed communications. A second form of humor emerges in the context of captivity, consisting of throwing feces at human onlookers—who often respond to this with laughter. This early form of humor shows up in signing apes in the form of calling others “dirty,” a sign associated with feces. The diversity of forms of signing humor shown by apes is linked to McGhee, Paul E.Humor: Its origin and development. San Francisco, CA: W. H. Freeman & Co, McGhee, Paul E.Understanding and promoting the development of children’s humor. Dubuque, IA: Kendall/Hunt. model of humor development.

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Comparison of Sign versus Verbal Symbol Training in Retarded Adults

Perceptual and Motor Skills ◽

10.2466/pms.1982.55.2.395 ◽

1982 ◽

Vol 55 (2) ◽

pp. 395-401 ◽

Cited By ~ 3

Author(s):

Kandace A. Penner ◽

William N. Williams

Keyword(s):

Sign Language ◽

Representative Sample ◽

Verbal Learning ◽

Mentally Retarded ◽

Language Training ◽

Combined Approach ◽

Verbal Language ◽

Verbal Labels ◽

Sign Training ◽

The Relationship

Sign language as an alternative or as an augmentive system to verbal language training in the mentally retarded is in widespread use. This study began an exploration of the relationship between sign and verbal learning in 10 institutionalized severely mentally retarded adults. Three experimental groups were taught color labels. Three persons received sign training only, 4 more received verbal training only, and last 3 received combined verbal and sign training. Sign labels tended to be learned more efficiently than verbal labels by this small group. Combined sign and verbal training improved verbal learning whereas sign learning was not improved through the combined approach. Replication and extension of this preliminary work with a larger and more representative sample is needed.

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AI at the Edge for Sign Language Learning Support

International Journal of Humanized Computing and Communication ◽

10.35708/hcc1868-148363 ◽

2020 ◽

Vol 1 (1) ◽

pp. 23-42

Author(s):

Pietro Battistoni

Keyword(s):

Neural Networks ◽

Language Learning ◽

American Sign Language ◽

Sign Language ◽

Fog Computing ◽

Learning Support ◽

Language Training ◽

End User ◽

Robust Model ◽

Computational Resources

In the field of multimodal communication, sign language is and continues to be, one of the most understudied areas. Thanks to the recent advances in the field of deep learning, there are far-reaching implications and applications that neural networks can have for sign language mastering. This paper describes a method for ASL alphabet recognition using Convolutional Neural Networks (CNN), which allows to monitor user’s learning progress. American Sign Language (ASL) alphabet recognition by computer vision is a challenging task due to the complexity in ASL signs, high interclass similarities, large intraclass variations, and constant occlusions. We produced a robust model that classifies letters correctly in a majority of cases. The experimental results encouraged us to investigate the adoption of AI techniques to support learning of a sign language, as a natural language with its own syntax and lexicon. The challenge was to deliver a mobile sign language training solution that users may adopt during their everyday life. To satisfy the indispensable additional computational resources to the locally connected end- user devices, we propose the adoption of a Fog-Computing Architecture.

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