1. Conati, Cristina, Sébastien Lallé, Md Abed Rahman, and Dereck Toker [ In Alphabetical Order]. "Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks." ACM Transactions on Interactive Intelligent Systems (TiiS) 10, no. 2 (2020): 1-41. [Link]
Cristina Conati, Sébastien Lallé, Md. Abed Rahman, Dereck Toker [ In Alphabetical Order], Further Results on Predicting Cognitive Abilities for Adaptive Visualizations , Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence(IJCAI-17). [Link]
Toward user-adaptive visualizations: further results on real-time prediction of user cognitive abilities from action and eye-tracking data , Masters Thesis, UBC.
Abstract: Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising user-adaptive visualizations that can adapt to a user’s abilities as needed during the interaction. In this thesis, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from gaze data, thus providing evidence on the generality of these findings. We also evaluate how quality of gaze data impacts prediction. Finally, we further extend previous work by investigating interaction data as an alternative source to predict our target user characteristics. We present a formal comparison of predictors based solely on gaze data, on interaction data, or on a combination of the two. [Complete Thesis]
An Integrated Lifelogging System for Android, Undergraduate Thesis, IUT
Abstract: The advent and proliferation of mobile devices have enabled us with the capability to store and make sense of passively gathered records of everyday human activities. This is called lifelogging. Every mobile device currently comes with a range of sensing abilities which includes but not limited to a camera, accelerometer, gps, digital compass etc. All of these can be used to gather data unobtrusively. This data can then be made sense of by leveraging cloud computing. In this work we build towards making a complete smartphone based lifelogging system, one that unobtrusively saves data as well as shows how it can be used to benefit the user. Another focus of lifelogging is to promote reminiscence within its users. However, being able to find a good way to trigger memories is a challenge in itself. In this work, we also explore the possibility of using music and background noise as a memory recalling tool and see the implications that it can have on reminiscence in a smartphone based lifelogger.