Frontiers in bioengineering and biotechnology · 2026
Prediction-driven active assistance control for lower-limb exoskeletons using multisource information fusion and short-term gait prediction.
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Guo Yuanxiang, Yang Yuhui, Zhao Wenyu, Huang Zhijue, Gao Xueshan, Han Xin, Deng Junlin, Zhao Peng
Аннотация
INTRODUCTION: For lower-limb exoskeletons to provide continuous position-reference assistance based on wearer state, the state of human-exoskeleton coupling must be sensed and translated into executable motor commands. However, when prediction outputs are directly fed into low-level controllers without further processing, command discontinuities and increased human-exoskeleton interaction torque may occur because of sensor noise or gait-phase transitions. Multisource information fusion and short-horizon gait prediction are therefore regarded as effective means of addressing this problem.
METHODS: In this study, a prediction-driven position-reference control method based on multisource information fusion and a CNN-BiLSTM-GTN model is proposed. Surface electromyography (sEMG), inertial measurement unit (IMU) signals, plantar pressure, joint angles and angular velocities, and human-exoskeleton interaction torques are synchronously collected and organized as sliding-window inputs. In the developed CNN-BiLSTM-GTN model, local dynamic feature extraction by convolutional neural networks, temporal dependency modelling by bidirectional long short-term memory networks, and hip-knee synergistic coupling representation by a graph transformation network are integrated to enable accurate short-term prediction of hip and knee joint-angle trajectories. To make the predicted angles executable, a prediction-driven position-reference assistive strategy is designed. The predicted rolling position references are corrected using measured joint states, gait phase, plantar support state, and interaction torque, thereby generating smooth and bounded joint angle commands.
RESULTS: Experimental results show that the proposed model achieved higher prediction accuracy than the evaluated prediction baselines. The closed-loop experiment further showed that the predicted trajectories could be converted into continuous and bounded position commands with stable tracking.
DISCUSSION: During the evaluated trials, the measured interaction torques remained below the predefined controller intervention threshold.
METHODS: In this study, a prediction-driven position-reference control method based on multisource information fusion and a CNN-BiLSTM-GTN model is proposed. Surface electromyography (sEMG), inertial measurement unit (IMU) signals, plantar pressure, joint angles and angular velocities, and human-exoskeleton interaction torques are synchronously collected and organized as sliding-window inputs. In the developed CNN-BiLSTM-GTN model, local dynamic feature extraction by convolutional neural networks, temporal dependency modelling by bidirectional long short-term memory networks, and hip-knee synergistic coupling representation by a graph transformation network are integrated to enable accurate short-term prediction of hip and knee joint-angle trajectories. To make the predicted angles executable, a prediction-driven position-reference assistive strategy is designed. The predicted rolling position references are corrected using measured joint states, gait phase, plantar support state, and interaction torque, thereby generating smooth and bounded joint angle commands.
RESULTS: Experimental results show that the proposed model achieved higher prediction accuracy than the evaluated prediction baselines. The closed-loop experiment further showed that the predicted trajectories could be converted into continuous and bounded position commands with stable tracking.
DISCUSSION: During the evaluated trials, the measured interaction torques remained below the predefined controller intervention threshold.