Abstract
Background
Sports-specific three-dimensional pose data provide an opportunity to characterize the dynamics of athletic movement through joint trajectories and their temporal variation. This study investigated whether interpretable kinematic representations derived from 3D skeleton sequences can distinguish athletic movement classes and how their performance compares with topology-aware graph representations.
Methods
AthletePose3D data from five subjects were analyzed using 18 movement labels derived from the available sequence annotations. H36M-17 pose sequences were root-centered and scale-normalized, and movement was represented using sequence-level features describing joint position, spatial range, velocity, acceleration, motion magnitude, and body-region relationships. These features characterize observable movement dynamics relevant to the study of coordinated human motion, but were not used to infer physiological mechanisms, injury risk, or causal biomechanics.
Results
Interpretable kinematic representations consistently provided stronger movement-classification performance than the evaluated graph-based representations. The original class-balanced Random Forest achieved a mean macro-F1 of 0.854, and subsequent evaluation with a stronger tree-based classifier further improved performance, whereas fixed and adaptive graph approaches remained below the leading kinematic models. Model attribution indicated that discriminative information was distributed unevenly across body regions, with greater contributions from the lower limbs and selected upper-limb features, consistent with the multi-segment coordination required by the athletic movements represented in the dataset. Robustness and subject-domain-shift analyses further showed that classification performance depended on pose completeness and subject-action coverage.
Conclusion
These findings demonstrate that normalized 3D skeletal kinematics can provide an interpretable representation of athletic movement dynamics for computational movement classification. The results support the use of pose-derived kinematic features for studying patterns of human movement while remaining distinct from direct physiological measurement or causal biomechanical inference.