—Lifelogging is a phenomenon whereby an individual digitally records his/her personal life experiences, for a variety of purposes. Activity recognition and segmentation is fundamental to many of the use cases in lifelogging. However, detecting sufficiently robust user activity boundaries that could be deployed with confidence in a subjective real-world setting remains a challenge. In this paper, we extend our previous work on identifying a better activity recognition and segmentation approach to multimodal lifelog data, primarily through the introduction of automatic thresholding techniques, but also through revising the criteria for selecting the most appropriate size of sliding window when evaluating the proposed algorithms. We use an open and publicly available lifelog test collection over a time period of 27 days with manual annotations and manually groundtruthed activities.