Kinetic Human Movement Ontology: a semantic terminology model to symbolically represent physiological movement

Cataloging physiological and anatomical terminologies may provide a fundamental basis for bridging the involvement of muscles, skeletal, and other essential parts in human movement, in addition to understanding their role on human morbidity. We present a general formalized ontology model that focuses on anatomical and physiological entities that are active during human movement, called the Kinetic Human Movement Ontology (KHMO). We developed a model to represent human movement that links human postures using OWL2. We used the model to assemble various open-sourced controlled terminologies involved in human movement - anatomical entities, physiological terms, etc. KHMO presents 1954 classes, 42 properties, and 1921 logical axioms, and passed logical satisfiability and consistency tests. Compared to physical activity-related ontologies, KHMO exhibited high semiotic quality, including high domain coverage. KHMO is publicly available as an open-source resource on our GitHub repository, and we provide software for data management of our ontology model. We envision furthering the research of our work to interoperate with human data movement and for data curation and harmonization endeavors.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-03-07
DOI
https://doi.org/10.1038/s41597-026-06984-z
Primary Topic
Action Observation and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Kinetic Human Movement Ontology: a semantic terminology model to symbolically represent physiological movement

Muhammad Amith, Eloisa Nguyen, Licong Cui, Yang Gong et al.
Scientific Data
Action Observation and Synchronization
article

Kinetic Human Movement Ontology: a semantic terminology model to symbolically represent physiological movement

Muhammad Amith, Eloisa Nguyen, Licong Cui, Yang Gong, Ahsiya Lin, Vivian Ha, Cui Tao
article en

Abstract

Cataloging physiological and anatomical terminologies may provide a fundamental basis for bridging the involvement of muscles, skeletal, and other essential parts in human movement, in addition to understanding their role on human morbidity. We present a general formalized ontology model that focuses on anatomical and physiological entities that are active during human movement, called the Kinetic Human Movement Ontology (KHMO). We developed a model to represent human movement that links human postures using OWL2. We used the model to assemble various open-sourced controlled terminologies involved in human movement - anatomical entities, physiological terms, etc. KHMO presents 1954 classes, 42 properties, and 1921 logical axioms, and passed logical satisfiability and consistency tests. Compared to physical activity-related ontologies, KHMO exhibited high semiotic quality, including high domain coverage. KHMO is publicly available as an open-source resource on our GitHub repository, and we provide software for data management of our ontology model. We envision furthering the research of our work to interoperate with human data movement and for data curation and harmonization endeavors.

Scientific Data
Seattle Pacific University (US), Jacksonville College (US), Clinical Research Solutions (US), Mayo Clinic in Florida (US), The University of Texas Health Science Center (US), The University of Texas Medical Branch at Galveston (US), Rice University (US), The University of Texas Health Science Center at Houston (US)
Cancer Prevention and Research Institute of Texas, University of Texas Health Science Center at Houston, National Institutes of Health
Reduced inequalities
Openalex Percentile: Top 40%
Action Observation and Synchronization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.