Prediction of Contralateral Lower-Limb Joint Angles Using Vibroarthrography and Surface Electromyography Signals in Time-Series Network

作者：Can Wang, Bailin He, Wenhao Wei, Zhengkun Yi, Pengbo Li, Shengcai Duan, Xinyu Wu · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2022 · DOI：10.1109/tase.2022.3185706 · 被引用次数：41 · 研究领域：Muscle activation and electromyography studies、Stroke Rehabilitation and Recovery、Prosthetics and Rehabilitation Robotics

Multisource biosignals are being increasingly used in human–machine interaction applications. In particular, a critical problem in exoskeleton-assisted rehabilitation for patients with hemiplegia is the generation of rhythmic and symmetrical locomotion. To support lower limb rehabilitation, we propose a model for predicting contralateral joint angles using multisource biosignals. First, a vibroarthrography (VAG) sensor is attached to the affected leg, and surface electromyography sensors are attached to the sound leg. The corresponding signals are used to estimate the hip, knee, and ankle joint angles of the affected leg. Second, an algorithm based on a temporal convolution network (TCN) is introduced to predict the contralateral lower-limb joint angles during human locomotion. The TCN is compared with a long short-term memory (LSTM) network and a convolutional neural network. Experiments were conducted by 10 healthy participants. The results of the proposed model were compared with measurements from encoders in three joints mounted on an exoskeleton to verify the applicability of the proposed model. In addition, by using the outputs of a motion capture system as the ground truth, the experimental results validated the model prediction performance. The prediction root mean square error (RMSE) of the TCN was 52%–70% lower than that of the LSTM network and CNN at different paces. The prediction RMSE using VAG was 20%–24% lower than that without using VAG. Note to Practitioners—...