Lower-Limb Kinematic Reconstruction from Surface Electromyography Across Locomotor Tasks Using Shared Muscle Synergies

Surface electromyography (sEMG) reflects neuromuscular control, but multichannel recordings are high-dimensional and difficult to interpret. This study evaluated whether muscle-synergy activations provide a compact input representation for lower-limb kinematic reconstruction. Twelve-channel sEMG and hip, knee, and ankle angles were obtained from 120 healthy male participants performing seven tasks in the Gait120 dataset. A four-synergy representation was derived using nonnegative matrix factorization, and within-participant cross-task similarity was assessed. Under participant-wise five-fold cross-validation, fold-specific shared dictionaries were estimated exclusively from training participants, and separate task-specific models reconstructed joint trajectories across five locomotor tasks. ExtraTrees achieved the lowest RMSE in most task–joint combinations and was used for exploratory detailed comparisons. Shared-synergy activations reduced the regressor-input representation from 12 variables to four activation coefficients per time point. Task-level mean RMSEs were 5.60–6.29° for synergy activations and 5.52–6.28° for raw sEMG. After Holm correction, no statistically significant difference was detected in 14 of the 15 task–joint comparisons, while one favored synergy activations. These findings support shared-synergy activation as a compact and physiologically interpretable input representation and provide a basis for neuromuscularly informed modeling of lower-limb movement across locomotor conditions.