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

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.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1145/3675094.3678479
Citations
2
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.69
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article

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

Jacob W. Kamminga, Rob van der Duim, Erik ten Hove
2 citations
Natural Language Processing Techniques
0.69
article

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

Jacob W. Kamminga, Rob van der Duim, Erik ten Hove
article en
2 citations

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.

University of Twente (NL)
Openalex Percentile: Top 24%
Natural Language Processing Techniques
0.69
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