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M-MOVE-IT: Multimodal Machine Observation and Video-Enhanced Integration Tool for Data Annotation

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Abstract

M-MOVE-IT is an open-source framework that simplifies data acquisition, annotation, and AI training for wearable technology. It addresses the challenges of synchronizing video and IMU data, making it easier to develop AI models for healthcare, sports, wildlife monitoring, anti-poaching, and livestock management applications. The framework automates and streamlines managing sensors, subjects, and deployments, synchronizing data, and annotating activities. M-MOVE-IT uses the real-time clocks of sensors and an offset annotation step to achieve precise synchronization, automatically parses sensor metadata, and generates annotation tasks. The export module provides data in JSON format for easy use in AI training. M-MOVE-IT's design supports active learning and human-in-the-loop development, enhancing the efficiency and scalability of wearable technology research.

Original languageEnglish
Title of host publicationUbiComp Companion 2024 - Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing
EditorsVassilis Kostakos, Judy Kay, Thuong Hoang
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages911-915
Number of pages5
ISBN (Electronic)979-8-4007-1058-2
DOIs
Publication statusPublished - 5 Oct 2024
EventACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2024 - Melbourne, Australia
Duration: 5 Oct 20249 Oct 2024

Conference

ConferenceACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2024
Abbreviated titleUbiComp 2024
Country/TerritoryAustralia
CityMelbourne
Period5/10/249/10/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Active learning (AL)
  • Activity recognition
  • Multimodal AI
  • Multimodal annotation
  • Sensor fusion

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