Clustering of shoulder movement patterns using K-means algorithm based on the shoulder range of motion.

This study aimed to classify and identify shoulder movement patterns based on shoulder joint range of motion (RoM) by applying the K-means clustering algorithm. Observational study using data from the 5th Size Korea Anthropometric Survey (2003–2004). Data analysis focused on anonymized shoulder RoM measurements from a national survey. Analysis included 541 participants after excluding those with incomplete shoulder RoM data. Identification of clusters based on measurements of shoulder flexion, extension, internal rotation, external rotation, horizontal adduction, and horizontal abduction. Eight distinct clusters were identified, each showing unique shoulder mobility characteristics. Clusters 1 and 5 had the lowest flexion ranges, whereas clusters 7 and 8 exhibited low internal rotation and horizontal adduction. Clusters 2 and 6 displayed the highest flexion and overall high flexibility, while clusters 3 and 4 presented moderate flexion with low horizontal adduction. This observational study categorized shoulder movement into eight clusters, revealing diverse mobility patterns across the general population. This clustering provides a basis for potential research into the correlation between specific movement patterns and musculoskeletal disorders, aiding in the development of targeted therapeutic strategies. • K-means clustering identified 8 unique shoulder movement pattern clusters based on range of motion. • Cluster analysis revealed diverse movement patterns, informing potential musculoskeletal risks. • Findings suggest relationships between specific movement patterns and potential shoulder syndromes. • Study highlights need for further research to link movement patterns with specific shoulder conditions. [ABSTRACT FROM AUTHOR]