2018
DOI: 10.2197/ipsjjip.26.362
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Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Statistical Input Stumble Detection

Abstract: Many older adults are interested in smartphones. However, most of them encounter difficulties in selfinstruction and need support. Text entry, which is essential for various applications, is one of the most difficult operations to master. In this paper, we propose Typing Tutor, an individualized tutoring system for text entry that detects input stumbles using a statistical approach and provides instructions. By conducting two user studies, we clarify the common difficulties that novice older adults experience … Show more

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Cited by 5 publications
(5 citation statements)
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“…Alternatively, the large-scale deep neural network model based on a single-channel convolutional neural network [19], [20] also attempts to integrate multiple multichannel information from the data layer, model layer and decision layer to achieve multitask learning [21] and crossmodal learning [22]. In addition, many other models are used for multichannel information fusion, such as multilayer support vector machines (SVMs) [23], [24], decision regression trees, random forests and other methods. Moreover, many scholars have applied the appeal model method to practical engineering, such as simulating human writing of text based on the dynamic Bayes model [25], understanding gestures and gestures based on the Markov decision process [26], and SVM-based identity differentiation [27].…”
Section: B Multichannel Human-computer Interaction Methodsmentioning
confidence: 99%
“…Alternatively, the large-scale deep neural network model based on a single-channel convolutional neural network [19], [20] also attempts to integrate multiple multichannel information from the data layer, model layer and decision layer to achieve multitask learning [21] and crossmodal learning [22]. In addition, many other models are used for multichannel information fusion, such as multilayer support vector machines (SVMs) [23], [24], decision regression trees, random forests and other methods. Moreover, many scholars have applied the appeal model method to practical engineering, such as simulating human writing of text based on the dynamic Bayes model [25], understanding gestures and gestures based on the Markov decision process [26], and SVM-based identity differentiation [27].…”
Section: B Multichannel Human-computer Interaction Methodsmentioning
confidence: 99%
“…Consequently, most of the research studies aim at resolving/assisting older adults with age-related issues through specific AI-enabled technological interventions [6,13,16]. For example, how conversational agents like Alexa, smart appliances like a smart cleaner [51], or assistive robots augment their physical or cognitive abilities [8,24,25,40,43,63], alleviate the social isolation felt by older adults [11,35], and their feelings towards it [3,44,49,56,58]. However, we still do not know older adults' general perceptions, experiences, and concerns related to AI-enabled technologies, which could ultimately play a significant role in setting their expectations of AI products and their decisions to embrace them.…”
Section: Older Adults and Aimentioning
confidence: 99%
“…To address the challenge of configuration, for example, Olwal et al [23] developed OldGen, a system that enables caregivers to customize the user interface layout and buttons on generic mobile phone platforms. To improve the accessibility of touchscreen keyboards for older adults, Toshiyuki et al [13] proposed Typing Tutor, a system that detects common mistakes and offers typing instructions based on the individual's mistakes. Older adults' typing proficiency increased with Typing Tutor, especially during the initial stages of learning.…”
Section: Mobile Accessibility For Older Adultsmentioning
confidence: 99%