We introduce OpenLifelogQA, a large-scale open-ended lifelog QA dataset constructed from 18 months of multimodal lifelog data. Lifelogging is the passive collection and analysis of personal daily activities using wearable devices, producing rich multimodal data such as images, locations, and biometrics. Question answering (QA) over lifelog data enables users to interactively query their own experiences, supporting applications in memory support, lifestyle analysis, and personal assistance. OpenLifelogQA contains 14,187 Q&A pairs spanning multiple question types and difficulty levels, designed to support robust evaluation in realistic settings. Compared with prior resources, OpenLifelogQA offers greater diversity and practicality for real-world applications. To establish baselines, we evaluate the LLaVA-NeXT-Interleave 7B model, achieving 89.7% BERTScore, 25.87% ROUGE-L, and an average LLM Score of 3.97. By releasing OpenLifelogQA, we aim to promote future research on lifelog technologies, paving the way for personal lifelog assistants capable of memory augmentation, healthcare support, and lifestyle coaching.

翻译：我们提出OpenLifelogQA，这是一个基于18个月多模态生活记录数据构建的大规模开放式生活记录问答数据集。生活记录是通过可穿戴设备被动收集和分析个人日常活动，产生图像、位置和生物特征等丰富多模态数据的过程。基于生活记录数据的问答（QA）使用户能够交互式查询自身经历，支持记忆辅助、生活方式分析和个人助理等应用。OpenLifelogQA包含14,187个问答对，涵盖多种问题类型和难度级别，旨在支持真实场景下的稳健评估。与先前资源相比，OpenLifelogQA为实际应用提供了更高的多样性和实用性。为建立基线，我们评估了LLaVA-NeXT-Interleave 7B模型，其BERTScore达89.7%，ROUGE-L为25.87%，平均LLM评分为3.97。通过发布OpenLifelogQA，我们旨在推动生活记录技术的未来研究，为具备记忆增强、健康护理支持和生活方式指导功能的个人生活记录助手铺平道路。