65 research outputs found

    Investigating the Usability of the Stylus Pen on Handheld Devices

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    Many handheld devices with stylus pens are available on the market, however, there have been few studies which examine the effects of the size of the stylus pen on user performance and subjective preferences for hand-held device interfaces. Two experiments were conducted to determine the most suitable dimensions (pen-length, pen-tip width and pen-width) for a stylus pen. In Experiment 1, five pen-lengths (7, 9, 11, 13, 15 cm) were evaluated. In Experiment 2, six combinations of three pen-tip widths (0.5, 1.0 and 1.5mm) and the two pen widths (4 and 7mm) were compared. In both experiments, subjects conducted pointing, steering and writing tasks on a PDA. The results were assessed in terms of user performance and subjective evaluations for all three pointing, steering and writing tasks. We determined that the most suitable pen dimensions were 11 cm for length, 0.5 mm for tip width, and 7mm for pen width

    How Are the Differences between Selection Strategies Affected by Changes in Target Size, Distance and Direction?

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    Fitt's law is commonly used to model target selection. But Fitts' law deals with only one kind of selection strategy. Our question is, do changes in target size, distance and direction to a target affect the differences in performance between target selection strategies? We performed the first empirical tests on a pen-based system to evaluate differences in performance between six selection strategies for selecting a target. Three target sizes, eight pen-movement-directions and three pen-movement-distances were applied to all six strategies. The results show that differences between selection strategies are affected by variations in target size but not by the other parameters (distance and direction)

    Designing interactions for the ageing populations – addressing global challenges

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    We are concurrently witnessing two significant shifts: digital devices are becoming ubiquitous, and older people are becoming a very large demographic group. However, despite the recent increase in related CHI publications, older adults continue to be underrepresented in HCI research as well as commercially. Therefore, the overarching aim of this workshop is to increase the momentum for such research within CHI and related fields such as gerontechnology. For this, we plan to create a space for discussing and sharing principles and strategies to design interactions and evaluate user interfaces (UI) for the ageing population. We thus welcome contributions of empirical studies, theories, design and evaluation of UIs for older adults. Building on the success of last two year’s workshops, we aim to grow the community of CHI researchers across borders interested in this topic by fostering a space to exchange results, methods, approaches, and ideas from research on interactive applications in support of older adults that are reflective of international diversity that is representative of CHI

    Rethinking mobile interfaces for older adults

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    This SIG advances the study of mobile user interfaces for the aging population. The topic is timely, as the mobile device has become the most widely used computer terminal and at the same time the number of older people will soon exceed the number of children worldwide. However, most HCI research addresses younger adults and has had little impact on older adults. Some design trends, like the mantra “smaller is smarter”, contradict the needs of older users. Developments like this may diminish their ability to access information and participate in society. This can lead to further isolation (social and physical) of older adults and increased widening of the digital divide. This SIG aims to discuss mobile interfaces for older adults. The SIG has three goals: (i) to map the state-of-art, (ii) to build a community gathering experts from related areas, and (iii) to raise awareness within the SIGCHI community. The SIG will be open to all at CHI

    Designing mobile interactions for the ageing populations

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    We are concurrently witnessing two significant shifts: mobiles are becoming the most used computing device; and older people are becoming the largest demographic group. However, despite the recent increase in related CHI publication, older adults continue to be underrepresented in HCI research as well as commercially, further widening the digital divide they face and hampering their social participation. This workshop aims to increase the momentum for such research within CHI and related fields such as gerontechnology. We plan to create a space for discussing and sharing principles and strategies to design and evaluate mobile user interfaces for the aging population. We thus welcome contributions to empirical studies, theories, design and evaluation of mobile interfaces for older adults

    Designing interactions for the ageing populations – addressing global challenges

    Get PDF
    We are concurrently witnessing two significant shifts: digital devices are becoming ubiquitous, and older people are becoming a very large demographic group. However, despite the recent increase in related CHI publications, older adults continue to be underrep- resented in HCI research as well as commercially. Therefore, the overarching aim of this workshop is to increase the momentum for such research within CHI and related fields such as gerontechnol- ogy. For this, we plan to continue developing a space to discuss and share principles and strategies to design interactions and evaluate user interfaces (UI) for the ageing population. We thus welcome contributions of proposing improved empirical studies, theories, de- sign and evaluation of UIs for older adults. Building on the success of the last three years’ workshops, we aim to grow the community of CHI researchers across borders interested in this topic by fos- tering a space to exchange results, methods, approaches, and ideas from research on interactive applications in support of older adults that are reflective of international diversity

    Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

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