Clustering of Shoulder Movement Patterns using K-means Algorithm Based on the Shoulder Range of Motion

ContextCategorization in medicine is used to enhance understanding of a disease or syndrome and apply it to treatment and is based on human clinical experience or theory. Cluster analysis using the K-means algorithm is an unsupervised machine learning method that classifies clusters based on numerical data. The purpose of this study was to classify subjects into clusters using K-means algorithm based on shoulder range of motion (ROM) and identify the characteristics of the clusters. DesignCross-sectional study Methods551 data samples measured in the 5th Size Korea Anthropometric Survey (2003[~]2004) were used. Clustering was performed using the K-means algorithm, and the appropriate number of clusters was determined using the elbow curve and silhouette score. The characteristics of the clusters were analyzed by comparing the average values of shoulder ROM in the clusters. ResultsThe appropriate number of classifications of clusters according to the shoulder ROM was 8. Clusters 1 and 5 had the lowest flexion range, and clusters 7 and 8 had low internal rotation and shoulder horizontal adduction ranges. Clusters 2 and 6 exhibited the highest flexion and overall high flexibility. Clusters 3 and 4 showed moderate flexion ranges but low horizontal adduction ranges. Shoulder movement patterns were classified into a total of 8 clusters according to the shoulder ROM. ConclusionBased on this clustering system, it was possible to identify the pattern of shoulder movement in ordinary people, and it could be used as basic data to identify and treat diseases or syndromes according to the pattern.