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.

This paper examines the effects of objective feedback of facial expression recognition on the operators of a video support chat system. We have built a facial expression feedback (FEF) system, which recognizes the users' facial expressions, and displays a visual summary of the recognition results to the user, in order to raise self-awareness of his/her expressions during video communication. We evaluated our system in a supposed simulation of a call center, in which operators provide assistance to customers about how to use their smartphones. Experimental results have verified that our approach is useful to help operators improve their self-awareness of facial expression in the video support chat.

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.

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.