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VIMES: A Wearable Memory Assistance System for Automatic Information Retrieval

Published: 12 October 2020 Publication History

Abstract

The advancement of artificial intelligence and wearable computing triggers the radical innovation of cognitive applications. In this work, we propose VIMES, an augmented reality-based memory assistance system that helps recall declarative memory, such as whom the user meets and what they chat. Through a collaborative method with 20 participants, we design VIMES, a system that runs on smartglasses, takes the first-person audio and video as input, and extracts personal profiles and event information to display on the embedded display or a smartphone. We perform an extensive evaluation with 50 participants to show the effectiveness of VIMES for memory recall. VIMES outperforms (90% memory accuracy) other traditional methods such as self-recall (34%) while offering the best memory experience (Vividness, Coherence, and Visual Perspective all score over 4/5). The user study results show that most participants find VIMES useful (3.75/5) and easy to use (3.46/5).

Supplementary Material

MP4 File (3394171.3413663.mp4)
In this video, we present VIMES, a wearable memory assistant for memory retrieval. We present the main findings of a collaborative design study that has been used in the process of building VIMES. Moreover, we discuss our design's main contributions to help future designers of wearable assistant systems, social acceptance, privacy, and intended adoption.

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cover image ACM Conferences
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 October 2020

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Author Tags

  1. augmented reality
  2. event detection
  3. face recognition
  4. memory assistance
  5. video
  6. wearable

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  • Research-article

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  • Research Grants Council of Hong Kongand the 5GEAR and FIT projects from Academy of Finland

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2024)Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory AugmentationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642450(1-18)Online publication date: 11-May-2024
  • (2023)What do Blind and Low-Vision People Really Want from Assistive Smart Devices? Comparison of the Literature with a Focus StudyProceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3597638.3608955(1-21)Online publication date: 22-Oct-2023
  • (2023)When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label LearningProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611846(1515-1526)Online publication date: 26-Oct-2023
  • (2023)SmartLog: A Smart TV-Based Lifelogging System for Capturing, Storing, and Visualizing Watching BehaviorInternational Journal of Human–Computer Interaction10.1080/10447318.2023.225005440:20(6232-6251)Online publication date: 29-Aug-2023
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