Supporting Human Memory by Reconstructing Personal Episodic Narratives from Digital Traces

TL;DR

This paper presents a method to reconstruct personal episodic narratives from heterogeneous digital traces, aiding memory recall and personal history understanding, with applications in healthcare and personal data analysis.

Contribution

It introduces a novel matching algorithm for grouping digital traces into episodes and a ranking technique for candidate episodes, advancing personal memory reconstruction.

Findings

Successfully integrates traces into coherent episodes

Enhances users' memory of past actions

Demonstrates effectiveness on real user data

Abstract

Numerous applications capture in digital form aspects of people's lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people's episodic memories and connect to their past personal events. This reconstruction has several applications, from helping patients with neurodegenerative diseases recall past events to gathering clues from multiple sources to identify recent contacts and places visited - a critical new application for the current health crisis. This paper takes steps towards integrating, connecting and summarizing the heterogeneous collection of data into episodic narratives using scripts - prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups several digital traces from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes. We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction technique 1) successfully integrates and combines traces from different sources into coherent episodes, and 2) augments users' memory of their past actions.