Evidence map›Paper›PMID 39464170›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics2024

Developing a computational representation of human physical activity and exercise using open ontology-based approach: a Tai Chi use case.

Eloisa Nguyen, Rebecca Z Lin, Yang Gong, Cui Tao, Muhammad Tuan Amith

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Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Eloisa NguyenSeattle Pacific University, Seattle, WA United States.
Rebecca Z LinWashington University School of Medicine, St Louis, MO United States.
Yang GongMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX United States.
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL United States.
Muhammad Tuan AmithDepartment of Biostatistics and Data Science, Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX United States.

Funding

Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingRF1AG072799 · NIA · YALE UNIVERSITY · PI LIU, HONGFANG, TAO, CUI · 2021 to 2021
$2.4M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingR01AG072799 · NIA · YALE UNIVERSITY · PI HONGFANG LIU, Cui Tao · 2025 to 2026
$1.7M
NIA NIH HHS R01 AG072799NIA NIH HHS RF1 AG072799
6 · The paper itself

Abstract

Many studies have examined the impact of exercise and other physical activities in influencing the health outcomes of individuals. These physical activities entail an intricate sequence and series of physical anatomy, physiological movement, movement of the anatomy, etc. To better understand how these components interact with one another and their downstream impact on health outcomes, there needs to be an information model that conceptualizes all entities involved. In this study, we introduced our early development of an ontology model to computationally describe human physical activities and the various entities that compose each activity. We developed an open-sourced biomedical ontology called the Kinetic Human Movement Ontology that reused OBO Foundry terminologies and encoded in OWL2. We applied this ontology in modeling and linking a specific Tai Chi movement. The contribution of this work could enable modeling of information relating to human physical activity, like exercise, and lead towards information standardization of human movement for analysis. Future work will include expanding our ontology to include more expressive information and completely modeling entire sets of movement from human physical activity.

Indexed as

anatomyknowledge engineeringknowledge representationontologyphysiologysemantic technologysemantic web

Identifiers

PMID39464170
PMCPMC11503552

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.