Footprint Tracker: reviewing lifelogs and reconstructing daily experiences

TL;DR

Footprint Tracker is a web app designed to help users review lifelogs from visual, location, and communication data to improve the accuracy of reconstructing daily experiences, advancing memory-assisted experience sampling methods.

Contribution

The paper introduces a novel web application that integrates multiple lifelog data types to support memory-based reconstruction of daily experiences, demonstrating its potential in experience research.

Findings

Supports review of visual, location, and communication lifelogs

Facilitates more accurate reconstruction of daily experiences

Provides a tool for studying memory-assisted experience sampling

Abstract

With the increasing emphasis on how mobile technologies are experienced in everyday life, researchers are increasingly emphasizing the use of in-situ methods such as Experience Sampling and Day Reconstruction. In our line of research we explore the concept of Technology-Assisted Reconstruction, in which passively logged behavior data assist in the later reconstruction of daily experiences. In this paper we introduce Footprint tracker, a web application that supports participants in reviewing lifelogs and reconstructing their daily experiences. We focus on three kinds of data: visual (as captured through Microsoft's sensecam), location, and context (i.e., SMS and calls received and made). We describe how Footprint Tracker supports the user in reviewing these lifelogs and outline a field study that attempts to inquire into whether and how this data support reconstruction from memory.