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

Abstract

Objectives

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.

Design

Observational study using data from the 5th Size Korea Anthropometric Survey (2003–2004).

Setting

Data analysis focused on anonymized shoulder RoM measurements from a national survey.

Participants

Analysis included 541 participants after excluding those with incomplete shoulder RoM data.

Main Outcome Measures

Identification of clusters based on measurements of shoulder flexion, extension, internal rotation, external rotation, horizontal adduction, and horizontal abduction.

Results

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.

Conclusions

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.