Healthy Limb Driven Prediction for Real Time Control of Unilateral Exoskeletons in Gait Rehabilitation.

Abstract

Unilateral gait impairment after hemiparesis is characterized by pronounced asymmetry between limbs, which challenges trajectory generation in unilateral lower limb exoskeletons. Conventional mirroring strategies are limited in this setting and may transfer pathological compensations. This study uses healthy limb motion and a Temporal Convolutional Network with Attention (TCN-Attention) to reconstruct contralateral joint kinematics under causal and unilateral sensing constraints. In healthy subjects, the model achieved hip RMSE of $6.342^{\circ }$, knee RMSE of $6.813^{\circ }$, correlations above 0.90, and lower RMSE/MAE than recurrent baselines in the main comparison. A preliminary offline feasibility evaluation on six individuals after stroke showed temporal synchronization without individual fine tuning (phase error $\approx 10\%\text{--}12\%$). Because amplitude deviations remained in pathological gait, the predicted trajectories were treated as candidate references derived from healthy training patterns for supervised rehabilitation control.