Interaction With Tilting Gestures In Ubiquitous Environments

In this paper, we introduce a tilting interface that controls direction based applications in ubiquitous environments. A tilt interface is useful for situations that require remote and quick interactions or that are executed in public spaces. We explored the proposed tilting interface with different application types and classified the tilting interaction techniques. Augmenting objects with sensors can potentially address the problem of the lack of intuitive and natural input devices in ubiquitous environments. We have conducted an experiment to test the usability of the proposed tilting interface to compare it with conventional input devices and hand gestures. The experiment results showed greater improvement of the tilt gestures in comparison with hand gestures in terms of speed, accuracy, and user satisfaction.Comment: 13 pages, 10 figure

Onsetsu hyoki no kyotsusei ni motozuita Ajia moji nyuryoku intafesu ni kansuru kenkyu

制度:新 ; 報告番号:甲3450号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/10/26 ; 早大学位記番号:新577

Computer based writing support for dyslexic adults using language constraints

Computers have been used effectively to provide support for people with a variety of special needs. One such group is adults with dyslexia. Dyslexia is commonly recognised as a learning disorder characterised by reading, writing and spelling difficulties. It inhibits recognition and processing of graphic symbols, particularly those pertaining to language. Computers are a useful aid for dyslexic adults, especially word processors and their associated spelling tools. However, there are still areas where improvements are needed. Creating an environment, which minimises visual discomfort associated with proof reading and making selections from lists would be of benefit. Furthermore providing the correct type and level of support for spelling, grammar and sentence construction may result in higher standards being achieved. A survey of 250 dyslexic adults established their requirements and enabled the development of a specialist word processing system and associated spelling support tools. The hypothesis, that using a language with enforced structure and rigid constraints has a positive affect for dyslexic adults, was also tested. A support tool, which provided a controlled environment, to assist with sentence construction for dyslexic adults was developed from this. Three environments were created using the word processing system: environment 1 used the basic system with no support, environment 2 provided spelling support suggested by the survey subjects and environment 3 used the sentence constructing tool providing support and control. Using these environments in controlled experiments indicated that although environment 2 achieved high academic standards, environment 3 produced written work to an even higher standard and at the same time, the subjects derived greater satisfaction in using it. This research proves that working in a controlled, rigid environment, where structure is enforced, substantially benefits dyslexic adults performing computer-based writing tasks

D-Lexis: Alphabet Mobile Learning Application for dyslexia for dyslexia Based on Slingerland Methods of Learning

This dessertation reports on the development of D-Lexis, a mobile application with android platform to help dyslexic students in alphabetical learning based on Slingerland methods of learning. Dyslexia is a medical condition that hinders reading, language and spelling skills of a student which affects their learning performance and makes dyslexic students hate conventional classroom methods. Therefore, learning modules for dyslexia learning must be in sequence, structured and applies multisensory approach with focus in alphabet learning as fundamental of literacy to overcome problems of phonological processing that leads to offences in reading, writing, memory retention and spelling. This project focus at overcoming 3 offenses such as reversal and inversion of letters while writing, short memory retention and 'dancing letters' conditions as well as confusion of letters while reading through 5 modules-recognizing capital and lowercase letters, tracing capital and lowercase letters and exercise. The development of the system is based on rapid prototype methodology which is flexible for author to ensure the application meeting user's requirements. An interview with the dyslexia practitioners, 3 observations through videos, 5 qualitative surveys and 3 revisions on design of the systems are conducted to ensure the application is meeting requirements and needs of a dyslexic. The application is developed in 2 phase with 3 amendments on designs and 2 amendments on the development process which results in 5 interactive modules aims on enhancing writing and recognition of letters according to dyslexic needs and requirements. The proposed system is not only suitable for dyslexic but also can be used as a pre-literacy learning application for pre-schools students or primary students

A method of storage and display of Chinese characters as graphic symbols

Not provided

Language acquisition

This project investigates acquisition of a new language by example. Syntax induction has been studied widely and the more complex syntax associated with Natural Language is difficult to induce without restrictions. Chomsky conjectured that natural languages are restricted by a Universal Grammar. English could be used as a Universal Grammar and Punjabi derived from it in a similar way as the acquisition of a first language. However, if English has already been acquired then Punjabi would be induced from English as a second language. [Continues.

Proceedings of the 4th international conference on disability, virtual reality and associated technologies (ICDVRAT 2002)

The proceedings of the conferenc

D-Lexis: Alphabet Mobile Learning Application for dyslexia for dyslexia Based on Slingerland Methods of Learning

This dessertation reports on the development of D-Lexis, a mobile application with android platform to help dyslexic students in alphabetical learning based on Slingerland methods of learning. Dyslexia is a medical condition that hinders reading, language and spelling skills of a student which affects their learning performance and makes dyslexic students hate conventional classroom methods. Therefore, learning modules for dyslexia learning must be in sequence, structured and applies multisensory approach with focus in alphabet learning as fundamental of literacy to overcome problems of phonological processing that leads to offences in reading, writing, memory retention and spelling. This project focus at overcoming 3 offenses such as reversal and inversion of letters while writing, short memory retention and 'dancing letters' conditions as well as confusion of letters while reading through 5 modules-recognizing capital and lowercase letters, tracing capital and lowercase letters and exercise. The development of the system is based on rapid prototype methodology which is flexible for author to ensure the application meeting user's requirements. An interview with the dyslexia practitioners, 3 observations through videos, 5 qualitative surveys and 3 revisions on design of the systems are conducted to ensure the application is meeting requirements and needs of a dyslexic. The application is developed in 2 phase with 3 amendments on designs and 2 amendments on the development process which results in 5 interactive modules aims on enhancing writing and recognition of letters according to dyslexic needs and requirements. The proposed system is not only suitable for dyslexic but also can be used as a pre-literacy learning application for pre-schools students or primary students

Intelligent Techniques to Accelerate Everyday Text Communication

People with some form of speech- or motor-impairments usually use a high-tech augmentative and alternative communication (AAC) device to communicate with other people in writing or in face-to-face conversations. Their text entry rate on these devices is slow due to their motor abilities. Making good letter or word predictions can help accelerate the communication of such users. In this dissertation, we investigated several approaches to accelerate input for AAC users. First, considering that an AAC user is participating in a face-to-face conversation, we investigated whether performing speech recognition on the speaking-side can improve next word predictions. We compared the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines. We found that despite recognition word error rates of 7-16%, our ensemble of n-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts. In a user study with 160 participants, we also found that increasing number of prediction slots in a keyboard interface does not necessarily correlate to improved performance. Second, typing every character in a text message may require an AAC user more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce an AAC user\u27s physical input effort. We designed a recognizer optimized for expanding noisy abbreviated input where users often omitted spaces and mid-word vowels. We showed using neural language models for selecting conversational-style training text and for rescoring the recognizer\u27s n-best sentences improved accuracy. We found accurate abbreviated input was possible even if a third of characters was omitted. In a study where users had to dwell for a second on each key, we found sentence abbreviated input was competitive with a conventional keyboard with word predictions. Finally, AAC keyboards rely on language modeling to auto-correct noisy typing and to offer word predictions. While today language models can be trained on huge amounts of text, pre-trained models may fail to capture the unique writing style and vocabulary of individual users. We demonstrated improved performance compared to a unigram cache by adapting to a user\u27s text via language models based on prediction by partial match (PPM) and recurrent neural networks. Our best model ensemble increased keystroke savings by 9.6%