Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japane...Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.展开更多
The current work presents a support tutoring program on the Japanese language applied to 12 newly arrived Brazilian students who moved to Japan after their parents migrated in order to work in electronic factories. Al...The current work presents a support tutoring program on the Japanese language applied to 12 newly arrived Brazilian students who moved to Japan after their parents migrated in order to work in electronic factories. All of them were not able to speak Japanese, even the greetings; during lunch time they also had to eat some types of food they had never had before. Certainly, it was a daily battle against the unknown as they interfaced between two very different cultures. This support tutoring program was implemented in a Japanese public school in Taiwa Town, Miyagi Prefecture, and it aimed to create an adequate environment that could provide a teaching-learning process in the cognitive, emotional, and social aspects to prepare the students to live in Japan, as well as to prepare them for their return to Brazil in the future.展开更多
基金supported by the Competitive Research Fund of the University of Aizu,Japan.
文摘Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
文摘The current work presents a support tutoring program on the Japanese language applied to 12 newly arrived Brazilian students who moved to Japan after their parents migrated in order to work in electronic factories. All of them were not able to speak Japanese, even the greetings; during lunch time they also had to eat some types of food they had never had before. Certainly, it was a daily battle against the unknown as they interfaced between two very different cultures. This support tutoring program was implemented in a Japanese public school in Taiwa Town, Miyagi Prefecture, and it aimed to create an adequate environment that could provide a teaching-learning process in the cognitive, emotional, and social aspects to prepare the students to live in Japan, as well as to prepare them for their return to Brazil in the future.