Reduced-order analysis for gait kinematics of paraplegic patients based on spatiotemporal mode decomposition.

Complex human motion can be regarded as a summation of simple motion patterns. Existing studies have described kinematic synergy patterns with principal component analysis (PCA), but it overlooks time-frequency characteristics of gait. This study establishes a reduced-order spatiotemporal framework to obtain nonlinear kinematic patterns. Three paraplegic patients and twelve healthy subjects are recruited to conduct gait experiments. In the spatial domain, kinematic synergy modes of lower-limb joints are extracted by PCA. The principal modes at the instants of paraplegic patients' prominent joint torques are determined by calculating the contribution rate of weight. In the temporal domain, weights of principal modes are decomposed into nonlinear intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). The obtained kinematic synergy modes of healthy subjects and the mild patient are consistent with results obtained from dynamic mode decomposition (DMD). An additional principal mode is observed in one of the analyzed paraplegic participants and dominated at the instants of prominent joint torque. The dominant frequency of the principal IMF for this participant is 5.44 times the stride frequency. The proposed PCA-EMD framework provides a quantitative description of patient-specific kinematic synergy patterns and altered temporal movement dynamics in gait.