CASES: A Cognition-Aware Smart Eyewear System for Understanding How People Read
Authors
"The process of reading has attracted decades of scientific research. Work in this field primarily focuses on using eye gaze patterns to reveal cognitive processes while reading. However, eye gaze patterns suffer from limited resolution, jitter noise, and cognitive biases, resulting in limited accuracy in tracking cognitive reading states. Moreover, using sequential eye gaze data alone neglects the linguistic structure of text, undermining attempts to provide semantic explanations for cognitive states during reading. Motivated by the impact of the semantic context of text on the human cognitive reading process, this work uses both the semantic context of text and visual attention during reading to more accurately predict the temporal sequence of cognitive states. To this end, we present a Cognition-Aware Smart Eyewear System (CASES), which fuses semantic context and visual attention patterns during reading. The two feature modalities are time-aligned and fed to a temporal convolutional network based multi-task classification deep model to automatically estimate and further semantically explain the reading state timeseries. CASES is implemented in eyewear and its use does not interrupt the reading process, thus reducing subjective bias. Furthermore, the real-time association between visual and semantic information enables the interactions between visual attention and semantic context to be better interpreted and explained. Ablation studies with 25 subjects demonstrate that CASES improves multi-label reading state estimation accuracy by 20.90% for sentence compared to eye tracking alone. Using CASES, we develop an interactive reading assistance system. Three and a half months of deployment with 13 in-field studies enables several observations relevant to the study of reading. In particular, observed how individual visual history interacts with the semantic context at different text granularities. Furthermore, CASES enables just-in-time intervention when readers encounter processing difficulties, thus promoting self-awareness of the cognitive process involved in reading and helping to develop more effective reading habits." https://doi.org/10.1145/3610910
Research Questions / Practical Problems
Question signals indexed for this paper.
- 75%
MindNavigator: Exploring the Stress and Self-Interventions for Mental Wellness
CHI '18· Mental Health Apps & Online Support Communities
- 60%
Designing Mental Health Technologies that Support the Social Ecosystem of College Students
CHI '20· Mental Health Apps & Online Support Communities +1
- 60%
Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students' Mental Health
CHI '22· Explainable AI (XAI) +1
- 60%
Capturing the College Experience: A Four-year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic
UbiComp '24· Mental Health Apps & Online Support Communities +1
Based on Jaccard similarity of research subtopics & professions (≥60%)