The progress of lifelog research has enabled individuals to comprehensively capture their daily experiences. As a result, previous studies have primarily focused on developing tools to organise and retrieve lifelog moments effectively. However, existing lifelog data often lacks the ability to capture the lifelogger’s focal points (their attention), despite providing information-rich first-person-view lifelog images of their surroundings and activities. Consequently, this limitation hinders the lifelog retrieval systems’ utility when lifeloggers seek to retrieve specific information they have previously encountered. To address this research gap, a subjective point of view, represented through eye movements, should be incorporated as a new modality into lifelog data, thereby enhancing the retrieval performance. In pursuit of this objective, this dissertation investigates the feasibility of retriving on-screen information by analysing lifelogger’s reading activities and comprehension level. The primary contributions of this dissertation are as follows. Firstly, the development of LifeSeeker, an advanced interactive lifelog retrieval system, is developed and benchmarked in numerous lifelog retrieval challenges and competitions. By efficiently integrating various modality processing components (e.g., visual, text, location, biometrics) and user interaction components (e.g., search, filtering, browsing, relevance feedback) into a single interactive retrieval framework, LifeSeeker ranked among the top systems in these benchmarking activities, serving as the foundation for the rest of the thesis. Secondly, a novel reading comprehension dataset was created to explore the feasibility of recognising reading activities and estimating reading comprehension levels in daily life. Statistical tests and machine learning analyses on the dataset have revealed the strong connection between eye movement patterns, reading conditions, and reading comprehension. This led to a novel method for estimating reading comprehension with potential real-world applications. Furthermore, the longitudinal aspect of reading comprehension was investigated to examine the stability and generalisation of reading comprehension estimation models over time. Lastly, the reading comprehension estimation model was integrated into LifeSeeker as a new modality processor, resulting in a significant improvement in the system’s overall retrieval performance. In summary, this dissertation contributes to the understanding of reading activities and reading comprehension in real-world settings and showcases the potential of integrating reading comprehension estimation to enhance the retrieval of previously encountered information in lifelog data.