OpenLifelogQA: An Open-Ended Multi-Modal Lifelog Question-Answering Dataset

Lifelogging refers to the process of passively collecting, storing, and analysing personal daily life data using wearable devices. This data can support applications in memory preservation and enhancement. For example, using an ask-and-answer strategy, question-answering (QA) on lifelog data opens an interactive and interesting way to explore memorable events and insights into daily life. However, research resources for QA on lifelog data are limited to small-sized or synthetic QA datasets. In this paper, we present a novel lifelog QA dataset called OpenLifelogQA, building upon an 18-month lifelog dataset. Our dataset focuses on an open-ended and practical QA with real-world application in daily lifelog usage. We construct 14,187 pairs of Q&A with diverse types and difficulty levels. A baseline experiment is reported for this dataset with competitive average performance of 89.7% BERT Score, 25.87% ROUGE-L and 3.9665 LLM Score from LLaVA-NeXT-Interleave 7B model. We release this Q&A dataset to the research community to support new research into lifelog technologies, such as enabling personal chat-based assistants for lifelog data to become a reality.