Automated Help System For Novice Older Users From Touchscreen Gestures

Older adults who have never used smartphone often suffers from getting used to smartphone gestures because of their lack of basic knowledge or skills with the latest technologies like gesture-oriented touchscreens. In this paper, we propose a user modeling method for inferring problems novice users face for smartphone from their touchscreen gestures. The output of user model is used by automated help enabling them to acquire touchscreen gestures. We apply a feature extraction approach based on the frequent pattern mining of gesture sequence to the user modeling. The learned user model detects types of problems in real time and is used for automated help. To optimize of instruction timing and its selection, we use a Bayesian reinforcement learning approach, which balances the exploration-exploitation trade-off. We evaluate the effectiveness of the method by using a prototype assistant system for a map application. The evaluation with older (60+) novice users showed positive results. The performance of the prototype system and the potential for further application is discussed.