Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors

TL;DR

This paper introduces a new dataset and a method for predicting users' gaze behavior during mobile interactions using device sensors and wearable cameras, aiming to improve real-time attentive interfaces.

Contribution

It presents a novel long-term dataset of mobile interactions and a proof-of-concept forecasting method leveraging device and wearable sensor data.

Findings

Method can forecast bidirectional attention shifts

Predicts if focus is on mobile device

Demonstrates potential and challenges of attention forecasting

Abstract

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.