Augmenting Personalized Memory via Practical Multimodal Wearable Sensing in Visual Search and Wayfinding Navigation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perform various online processing tasks, such as dialing a phone number, recalling misplaced items' locations, or navigating through a store. However, inherent limitations in an individual's capacity to retain information often result in forgetting important details during such tasks. Although previous research has successfully utilized wearable and assistive technologies to enhance long-term memory functions (e.g., episodic memory), their application to supporting short-term recall in daily activities remains underexplored. To address this gap, we present Memento, a framework that uses multimodal wearable sensor data to detect significant changes in cognitive state and provide intelligent in situ cues to enhance recall. Through two user studies involving 15 and 25 participants in visual search navigation tasks, we demonstrate that participants receiving visual cues from Memento achieved significantly better route recall, improving approximately 20-23% compared to free recall. Furthermore, Memento reduced cognitive load and review time by 46% while also achieving a substantial reduction in computation time (3.86 secs vs. 15.35 secs), offering an average 75% effective compared to computer vision-based cues selection approaches.

Original languageEnglish (US)
Title of host publicationUMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages11-21
Number of pages11
ISBN (Electronic)9798400713132
DOIs
StatePublished - Jun 13 2025
Event33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025 - New York City, United States
Duration: Jun 16 2025Jun 19 2025

Publication series

NameUMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025
Country/TerritoryUnited States
CityNew York City
Period6/16/256/19/25

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Modeling and Simulation

Keywords

  • Affective Computing
  • Multimodal Sensing
  • Short-term Episode Recall
  • User Modeling
  • Verbal Cueing
  • Working Memory

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