For the increasing prevalence of large volumes of continuous multi-sensor personal data, new challenges have aroused such as multi-sensor data collection, lifelog data storage, meaningful information retrieval, and data representation across different retrieval systems. It leads a proper consideration of the segmentation of such large lifelogs into manageable discrete units as per the user information requirements. This research investigates the approaches to address the above challenges. The goal is to offer a novel and effective lifelog segmentation approaches to ad hoc search through lifelog archives. Instead of the conventional static indexing-time based segmentation of lifelog data, we proposed dynamic query-time based segmentation of multimodal lifelog data that we believe better satisfies the user information requirements. The main contributions of this dissertation are: First, identifying the process of constructing privacy-aware comparative reusable lifelog test collections including various challenges and principles, We introduce user’s issues such as user selection, user interrogation, data governance, data protection and ethics to test segmentation, and remembered information access; Second, defining the concept of documents in multimodal lifelog data retrieval; Third, defining the baseline segmentation approach to segment visual lifelog data, and developing state-of-the-art segmentation approach for multimodal lifelog data; Fourth, introducing a new dynamic query-time based segmentation algorithm for multimodal lifelog data. A publicly available dataset lifelog search challenge (LSC2018) is used for the comparative evaluation. We used different broad and narrow focused user queries, with promising results that outperform existing conventional static segmentation approaches. In summary, this dissertation primarily contributes to the field by identifying a new segmentation approach i.e. indexing-time or query-time to extract the meaningful information from multimodal lifelog data and highlights several potential topics of interest for the research community.