AirLog：让商场店铺级室内生活记录变得简单
AirLog: Store-Level Indoor Life Logging Made Easy
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- University of Georgia（佐治亚大学）
- University College London（伦敦大学学院）
- University of Toronto Mississauga（多伦多大学密西沙加分校）
- University of Pittsburgh（匹兹堡大学）
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中文总结 AI 辅助
AirLog 利用商场 Wi-Fi SSID 和导览图，结合惯性航位推算与大语言模型，自动重建店铺级轨迹并生成忠实的生活日志，显著优于现有基线。
中文摘要 AI 辅助
本文介绍了 AirLog，一个基于智能手机的生活日志系统，它能够自动重建用户在购物中心内的店铺访问记录，并将其汇总为人类可读的日志。与传统的室内定位系统不同，AirLog 避免了劳动密集型的无线电地图构建、专用的无线定位基础设施和算法校准。相反，它重新利用了商业空间中已有的两种线索：环境 Wi-Fi SSID 暴露的语义信息和室内导览图图像。AirLog 将导览图图像转换为空间地图，并将 Wi-Fi 语义锚点与惯性航位推算相融合，以恢复店铺级轨迹，随后由大语言模型（LLM）将其汇总为日志。这种店铺级的生活日志可以支持诸如个人记忆回忆、活动反思和自动日记生成等应用，而无需用户手动记录其去过的地方。我们在商用智能手机上实现了 AirLog，并在大规模公开数据集和自收集数据集上对其进行了评估。结果表明，与现有基线相比，AirLog 在店铺级区域恢复、语义匹配、轨迹重建和日志质量方面均有显著提升。一项人工评估进一步表明，生成的日志连贯且忠实于用户的访问记录。
英文摘要
This paper presents AirLog, a smartphone-based life journaling system that automatically reconstructs users' store visits in shopping malls and summarizes them into human-readable journals. Unlike conventional indoor localization systems, AirLog avoids labor-intensive radio-map construction and dedicated wireless localization infrastructure and algorithm calibrations. Instead, it repurposes two cues already available in commercial spaces: semantic information exposed by ambient Wi-Fi SSIDs and indoor directory images. AirLog converts directory images into spatial maps and fuses Wi-Fi semantic anchors with inertial dead reckoning to recover store-level trajectories, which are then summarized into journals by an LLM. Such store-level life logs can support applications such as personal memory recall, activity reflection, and automated diary generation without requiring users to manually record where they have been. We implement AirLog on commodity smartphones and evaluate it on both a large-scale public dataset and a self-collected dataset. The results demonstrate that AirLog substantially improves store-level region recovery, semantic matching, trajectory reconstruction, and journal quality over existing baselines. A human evaluation further shows that the generated journals are coherent and faithful to users' visits.
