Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network - Zhang, Song; Lu, Jiewei; Huo, Weiguang; Yu, Ningbo; Han, Jianda (Frontiers in Neurorobotics, 2022)
Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proved as an effective means for estimating human’s intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies have investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this paper, a feature-guided convolutional neural network (FG-CNN) is proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features are fused into a CNN model to guide CNN feature extraction, and both the handcrafted and CNN-extracted features are applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects are carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), are separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrate that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, the comparison studies have shown the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.