DCAF-Net: Dual-Channel Attentive Fusion Network for Lower Limb Motion Intention Prediction in Stroke Rehabilitation Exoskeletons

TL;DR

The paper tackles predicting lower-limb motion intention in stroke rehabilitation exoskeletons using pre-movement sEMG and IMU data. It introduces DCAF-Net, which fuses a dual-channel attentive EMG feature extractor and an IMU encoder with CNN and attention-LSTM, followed by a simple fusion classifier. On data from 11 participants (8 stroke, 3 healthy), it achieves 97.19% accuracy in stroke patients and 93.56% in healthy controls, with sub-200 ms latency. The results demonstrate robustness across affected limbs and show that multimodal fusion outperforms single modalities, enabling more responsive, intention-driven exoskeleton control.

Abstract

Rehabilitation exoskeletons have shown promising results in promoting recovery for stroke patients. Accurately and timely identifying the motion intentions of patients is a critical challenge in enhancing active participation during lower limb exoskeleton-assisted rehabilitation training. This paper proposes a Dual-Channel Attentive Fusion Network (DCAF-Net) that synergistically integrates pre-movement surface electromyography (sEMG) and inertial measurement unit (IMU) data for lower limb intention prediction in stroke patients. First, a dual-channel adaptive channel attention module is designed to extract discriminative features from 48 time-domain and frequency-domain features derived from bilateral gastrocnemius sEMG signals. Second, an IMU encoder combining convolutional neural network (CNN) and attention-based long short-term memory (attention-LSTM) layers is designed to decode temporal-spatial movement patterns. Third, the sEMG and IMU features are fused through concatenation to enable accurate recognition of motion intention. Extensive experiment on 11 participants (8 stroke subjects and 3 healthy subjects) demonstrate the effectiveness of DCAF-Net. It achieved a prediction accuracies of 97.19% for patients and 93.56% for healthy subjects. This study provides a viable solution for implementing intention-driven human-in-the-loop assistance control in clinical rehabilitation robotics.

Figures (6)

• Figure 1: The position of sEMG electrodes
• Figure 2: Extraction of pre-movement sEMG
• Figure 3: The structure of DCAF-Net
• Figure 4: Confusion matrixes of the prediction for patients
• Figure 5: Ablation experiment