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Machine learning for lumbar and pelvis kinematics clustering

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  • Seth Higgins
  • Sandipan Dutta
  • Rumit Singh Kakar

Abstract

Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorithm to use on time-series lumbar and pelvis kinematic data. Cluster evaluation measures such as silhouette coefficient, elbow method, Dunn Index, and gap statistic were used to evaluate the quality of decision making. The result show that multiple clustering evaluation methods should be used to determine the ideal number of clusters and algorithm suitable for clustering time-series data for each dataset being analyzed.

Suggested Citation

  • Seth Higgins & Sandipan Dutta & Rumit Singh Kakar, 2024. "Machine learning for lumbar and pelvis kinematics clustering," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(10), pages 1332-1345, July.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:10:p:1332-1345
    DOI: 10.1080/10255842.2023.2241593
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