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Clustering of shoulder movement patterns using K-means algorithm based on the shoulder range of motion
Gyeong-Tae Gwak1, Ui-Jae Hwang1, Jun-Hee Kim1
1Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, 1, Yeonsedae-gil, Maeji-ri, Heungeop-myeon, Wonju-si, Gangwon-do, 26493, Wonju, South Korea.
This study classified shoulder movement patterns into eight distinct clusters using range of motion data. These findings reveal diverse shoulder mobility in the general population, aiding future research on musculoskeletal disorders.
Area of Science:
- Biomechanics
- Human Movement Analysis
- Anthropometry
Background:
- Shoulder joint range of motion (RoM) is crucial for upper limb function.
- Understanding diverse shoulder mobility patterns is essential for identifying functional limitations and potential injury risks.
- Previous research has not comprehensively classified population-based shoulder movement patterns.
Purpose of the Study:
- To classify and identify distinct shoulder movement patterns.
- To utilize K-means clustering algorithm on shoulder range of motion (RoM) data.
- To establish a foundation for correlating movement patterns with musculoskeletal disorders.
Main Methods:
- An observational study utilizing data from the 5th Size Korea Anthropometric Survey (2003-2004).
- Analysis of anonymized shoulder RoM measurements from 541 participants.
- Clustering based on shoulder flexion, extension, internal rotation, external rotation, horizontal adduction, and horizontal abduction.
Main Results:
- Eight distinct clusters representing unique shoulder mobility characteristics were identified.
- Clusters varied significantly in flexion, internal rotation, and horizontal adduction capabilities.
- Specific clusters showed notably low or high ranges in particular movements, indicating diverse functional profiles.
Conclusions:
- This study successfully categorized general population shoulder movement into eight distinct clusters.
- The identified clusters highlight significant diversity in shoulder mobility.
- These findings can inform targeted therapeutic strategies and research into the relationship between movement patterns and musculoskeletal disorders.
