Our research seeks to improve the quality of user experience in ubiquitous HCI
nteraction) situations by enabling computers 1) to understand human capability in attention and cognition, 2) to augment our cognitive processing capabilities between physical and virtual information space, and 3) to create intelligent systems that adapt interventions to user difference.
To reach these goals, we use sensor-based analytics to understand in-situ human capability in attention and cognition and apply machine-learning techniques to time-series sensor data. Our approach combines cognitive science and psychology with mechatronics, machine learning, sensory augmentation, visual analytics, and hybrid assessment of cognitive load and user interruptibility.
We aim to create enabling technologies to solve real, everyday problems and to study how people interact with these technologies and AI-enabled products in connected environments. Specifically, our research will:
- Address quality of life issues for aging populations, including seniors who have difficulty interacting with virtual information spaces and AI-enabled products.
- Improve machine intelligence using cognitive science research related to executive control of working memory and differentiation of resource capacity. This work will help to create intelligent systems that adapt to our in-situ capabilities in attention and cognition.
- Improve the quality of HCI experience by developing real-time assessment methods, including multi-sensory interaction to facilitate rapid processing, to recognize behavioral patterns and in-situ context in users’ attention and cognition.