AirLog: Store-Level Indoor Life Logging Made Easy

2026-09-25

AI Summary

This study addresses the challenge that indoor localization in shopping malls typically relies on expensive infrastructure and manual mapping. We propose an automated, shop-level lifelogging method that requires no dedicated hardware. The approach innovatively repurposes ambient Wi-Fi SSID semantics as spatial anchors, constructs maps from directory images via computer vision, and reconstructs user trajectories by integrating inertial dead reckoning. Finally, a large language model generates human-readable lifelogs. Experiments on both public and self-collected datasets demonstrate that the proposed system significantly outperforms baseline methods. The generated logs are coherent and faithful to actual visitation paths, achieving low-cost, high-accuracy indoor localization and recording.

Abstract

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.

Problem

• Indoor localization
• Life logging
• Trajectory reconstruction
• Store-level tracking

Innovation

• Indoor Localization
• Life Logging
• Sensor Fusion
• Large Language Model
• Smartphone