Human-centric assist-as-needed strategy for Trunk Compensation Inhibition in post-stroke upper limb rehabilitation

Post-stroke patients frequently adopt compensatory trunk movements to accomplish tasks. This can lead to the development of abnormal movement patterns, which can hinder functional recovery. To address this issue, this study proposes a Human-Centric assist-as-needed strategy based on a upper limb rehabilitation robot. By constructing a closed-loop human-machine system, the exoskeleton assistance force is dynamically adjusted to inhibit compensatory trunk movements in hemiplegic patients while ensuring task completion. It employs Long Short-Term Memory (LSTM) networks to predict trunk compensation angles (R ² =0.99) and combines Convolutional Neural Networks with Bidirectional LSTM (CNN-BiLSTM) to calculate compensation torque (R ² =0.97), thereby achieving personalized and active torque assistance. Eleven patients with upper limb hemiplegia participated in the clinical trial. The results indicated that trunk compensation during training was significantly improved in all patients. Human-centric assist-as-needed strategy significantly reduces the compensatory angles of hemiplegic patients in the sagittal plane (76.79%), coronal plane (75.82%), and horizontal plane (87.47%) (p < 0.01), outperforming both the no intervention and task-centric strategies, with a 100% task completion rate during patient training. Compared to task-centric strategy, human-centric assist-as-needed strategy enhances patient intention consistency (IC) by 31.91% and increases average torque (AT) output by 48.02%. This study represents the first application of deep learning in the suppression of active trunk compensation for upper limb exoskeletons, providing an innovative approach to stroke rehabilitation.