# AirLog: Store-Level Indoor Life Logging Made Easy

arXiv: https://arxiv.org/abs/2609.31864 · HTML: https://mlsanity.com/paper/2609.31864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?](https://mlsanity.com/paper/2403.02727/) (2024)
- [Image Generators are Generalist Vision Learners](https://mlsanity.com/paper/2604.20329/) (2026)
- [FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans](https://mlsanity.com/paper/1804.00090/) (2018)
- [Can Large Vision Language Models Read Maps Like a Human?](https://mlsanity.com/paper/2503.14607/) (2025)
- [LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces](https://mlsanity.com/paper/2403.19857/) (2024)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App](https://mlsanity.com/paper/2404.00487/) (2024)
- [FloNa: Floor Plan Guided Embodied Visual Navigation](https://mlsanity.com/paper/2412.18335/) (2024)
- [Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments](https://mlsanity.com/paper/2105.08837/) (2021)
- [Towards Storytelling from Visual Lifelogging: An Overview](https://mlsanity.com/paper/1507.06120/) (2015)
- [Wi-Chat: Large Language Model Powered Wi-Fi Sensing](https://mlsanity.com/paper/2502.12421/) (2025)

## Abstract (arXiv)

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.