Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors
This addresses the challenge of fragmented visual attention for mobile interface designers, enabling proactive adaptations rather than reactive ones, though it is incremental as it builds on existing attentive UI concepts.
The paper tackles the problem of predicting users' gaze behavior during mobile interactions by introducing a proof-of-concept method that uses device-integrated and wearable sensors, demonstrating it can forecast attention shifts and predict focus on the device.
Visual attention is highly fragmented during mobile interactions, but the erratic nature of attention shifts currently limits attentive user interfaces to adapting after the fact, i.e. after shifts have already happened. We instead study attention forecasting -- the challenging task of predicting users' gaze behaviour (overt visual attention) in the near future. We present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). We propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate that our method can forecast bidirectional attention shifts and predict whether the primary attentional focus is on the handheld mobile device. We study the impact of different feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.