Toshiyuki Hagiya

Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Input Stumble Detection

Many older adults are interested in smartphones. However most of them encounter difficulties in self-instruction 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 and provides instructions. By conducting two user studies, we clarify the common difficulties that novice older adults experience and how skill level is related to input stumbles. Based on these studies, we develop Typing Tutor to support learning how to enter text on a smartphone. A two-week evaluation experiment with novice older adults (65+) showed that Typing Tutor was effective in improving their text entry proficiency, especially in the initial stage of use.

Probabilistic touchscreen keyboard incorporating gaze point information

We propose a novel probabilistic keyboard that takes into account the distance between a gaze point and a touch position in order to improve typing efficiency. The proposed keyboard dynamically changes the size of the search space for predicting candidate words based on a model that estimates the magnitude of touch position errors according to the distance between the gaze point and the touch position. This makes it possible for users to type intended words even when they glance at different areas on the screen. Performance was evaluated in terms of input accuracy in total error rate (TER) and of typing speed in words per minute (WPM). The results showed that the proposed keyboard successfully reduced the TER by 18.2% and increased WPM by 12.7% compared to the conventional keyboard.

Voice Input Tutoring System for Older Adults using Input Stumble Detection

Many older adults are interested in smartphones but encounter difficulties in self-instruction and need support, especially text input. Voice input is a useful option for text input, but also presents some difficulties for older adults.In this paper, we propose a tutoring system for voice input that detects input stumbles using a statistical approach and provides instructions to overcome them. We construct the tutoring system based on the data from a user study with novice older adults. In an evaluation experiment, the number of input stumble and the sentence completion time of the participants using the tutoring system were significantly smaller than those without it. The results showed that the tutoring system resulted in the improvement of the efficiency of voice input for novice older adults.

Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Statistical Input Stumble Detection

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 and how skill level is related to input stumbles with a 12-key layout for Japanese. Based on the study, we develop Typing Tutor to support learning how to enter text on a smartphone. A two-week evaluation experiment with novice older adults (65+) showed that Typing Tutor was effective in improving their text entry proficiency, especially in the initial stage of use. In addition, we demonstrate the applicability of Typing Tutor to other keyboards and languages with the QWERTY layout for Japanese and English.

Assistive Typing Application for Older Adults Based on Input Stumble Detection

Smartphones offer new opportunities to improve the lives of older adults. Although many older adults are interested in smartphones, most of them face difficulties in self-instruction and need support. Text entry, which is essential for various applications, is one of the most difficult operations to master. Therefore, we propose an assistive typing application that detects input stumbles and provides instructions for typing presented sentences, instead of having human tutors help older adults resolve the input stumbles by themselves. First, we investigated the ways that novice older adults have problems with text entry on smartphones. Next, we confirmed the acceptability of being provided with instructions for text entry by Wizard-of-Oz (WoZ). Then, we constructed an assistive typing application based on the collected data from two user studies. An evaluation with novice older adults (60+) showed that the assistive typing application increased typing speed by 17.2% and reduced input stumble incidence by 59.1% compared with the users’ initial performance. Improvement rates were almost the same as those achieved with human tutors.

HMM-Based Probabilistic Flick Keyboard Adaptable to Individual User

To provide an accurate and user-adaptable software keyboard for touchscreens, we propose a probabilistic flick keyboard based on hidden Markov models (HMMs). Touch and flick operations for each character are modeled by HMMs. This keyboard reduces input errors by taking the trajectory of the actual touch position into consideration and by user adaptation. We evaluated the performance of an HMM-based flick keyboard and maximum-likelihood linear regression (MLLR) adaptation. Experimental results showed that a user-dependent model reduced the error rate by 28.3%. In a practical setting, the MLLR adaptation to a specific user with only 10 words reduced the error rate by 16.6% and increased the typing speed by 11.9%.

Adaptable probabilistic flick keyboard based on HMMs

To provide an accurate and user-adaptable software keyboard for touchscreens, we propose a probabilistic flick keyboard based on HMMs. This keyboard can reduce the input error by taking the time series of the actual touch position into consideration and by user adaptation. We evaluated performance of the HMM-based flick keyboard and MLLR adaptation. Experimental results showed that a user-dependent model reduced the error rate by 28.2%. In a practical setting, MLLR user adaptation with only 10 words reduced the error rate by 16.5% and increased typing speed by 10.5%.