Towards attentive speed reading on small screen wearable devices
Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018•dl.acm.org
Smart watches can enrich everyday interactions by providing both glanceable information
and instant access to frequent tasks. However, reading text messages on a 1.5-inch small
screen is inherently challenging, especially when a user's attention is divided. We present
SmartRSVP, an attentive speed-reading system to facilitate text reading on small-screen
wearable devices. SmartRSVP leverages camera-based visual attention tracking and
implicit physiological signal sensing to make text reading via Rapid Serial Visual …
and instant access to frequent tasks. However, reading text messages on a 1.5-inch small
screen is inherently challenging, especially when a user's attention is divided. We present
SmartRSVP, an attentive speed-reading system to facilitate text reading on small-screen
wearable devices. SmartRSVP leverages camera-based visual attention tracking and
implicit physiological signal sensing to make text reading via Rapid Serial Visual …
Smart watches can enrich everyday interactions by providing both glanceable information and instant access to frequent tasks. However, reading text messages on a 1.5-inch small screen is inherently challenging, especially when a user's attention is divided. We present SmartRSVP, an attentive speed-reading system to facilitate text reading on small-screen wearable devices. SmartRSVP leverages camera-based visual attention tracking and implicit physiological signal sensing to make text reading via Rapid Serial Visual Presentation (RSVP) more enjoyable and practical on smart watches. Through a series of three studies involving 40 participants, we found that 1) SmartRSVP can achieve a significantly higher comprehension rate (57.5% vs. 23.9%) and perceived comfort (3.8 vs. 2.1) than traditional RSVP; 2) Users prefer SmartRSVP over traditional reading interfaces when they walk and read; 3) SmartRSVP can predict users' cognitive workloads and adjust the reading speed accordingly in real-time with 83.3% precision.
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