Internet of Things Technologies in Healthcare for People with Hearing Impairments

Alsharif, Bader; Ilyas, Mohammad

doi:10.1007/978-3-031-33545-7_21

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…This system would ease the process of communication between deaf and normal people. As a result, people with hearing impairments will have the opportunity to become more engaged in society, developing social interaction and relationships [ 3 , 4 ]. Nowadays, the sole means for people with hearing impairments to communicate with other ordinary people is through interpreters.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, not all hearing-impaired individuals use sign language as a method of communication, which may give them a sense of isolation and depression. Secondly, there are more than 200 different sign languages and dialects from different countries which may delay the process of implementing a sign language recognition system which would be applicable in various countries [ 4 , 5 ]. Lastly, not everyone is proficient in using today’s modern technology due to a lack of education and development, which can be neglected.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif,

Altaher,

Altaher

et al. 2023

Sensors

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Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%