The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

Rigid body orientation determined by IMU (Inertial Measurement Unit) is widely applied in robotics, navigation, rehabilitation, and human-computer interaction. In this paper, aiming at dynamically fusing quaternions computed from angular rate integration and FQA algorithm, a quaternion-based complementary filter algorithm is proposed to support a computationally efficient, wearable motion-tracking system. Firstly, a gradient descent method is used to determine a function from several sample points. Secondly, this function is used to dynamically estimate the fusion coefficient based on the deviation between measured magnetic field, gravity vectors and their references in Earth-fixed frame. Thirdly, a test machine is designed to evaluate the performance of designed filter. Experimental results validate the filter design and show its potential of real-time human motion tracking.

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact $236\pm 9$ ms in advance. Meanwhile, the proposed feature extraction method can achieve $90.71\pm 3.42\%$ accuracy of sEMG reconstruction and can guarantee $73.70\pm 5.01\%$ accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to $87.65\pm 3.83\%$ and decreases the angle prediction error from $4.{03}^{\circ }$ to $2.{36}^{\circ }$. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

The knee has gradually become an important research target for the lower extremity exoskeleton. However, the issue that whether the flexion-assisted profile based on the contractile element (CE) is effective throughout the gait is still a research gap. In this study, we first analyze the effective flexion-assisted method through the passive element’s (PE) energy storage and release mechanism. Specifically, ensuring assisting within an entire joint power period and the human’s active movement is a prerequisite for the CE-based flexion-assisted method. Second, we design the enhanced adaptive oscillator (EAO) to ensure the human’s active movement and the integrity of the assistance profile. Third, a fundamental frequency estimation based on discrete Fourier transform (DFT) is proposed to shorten the convergence time of EAO significantly. The finite state machine (FSM) is designed to improve the stability and practicality of EAO. Finally, we demonstrate the effectiveness of the prerequisite condition for the CE-based flexion-assisted method by using electromyography (EMG) and metabolic indicators in experiments. In particular, for the knee joint, CE-based flexion assistance should be within an entire joint power period rather than just in the negative power phase. Ensuring the human’s active movement will also significantly reduce the activation of antagonistic muscles. This study will aid in designing assistive methods from the perspective of natural human actuation and apply the EAO to the human-exoskeleton system.

Multiple sensors system has been adopted in most existing gait phase research. In this paper, we present a novel method to detect and discriminate gait phase based on single Inertial Measurement Unit (IMU). At first, we built a data acquisition system consisting of single IMU measuring the kinematic data of shank and foot switches labeling the data to collect the gait samples. And then we developed a gait phase recognition algorithm based on long short-term memory (LSTM) and trained it with phase-labeled data. The experimental results show that the algorithm has 91.4% accuracy on the testing set. Compared with existing approaches, the proposed method ensures the accuracy of gait phase detection and avoids the complex sensor system.

Inertial measurement units (IMUs) increasingly function as a basic component of a wearable sensor network (WSN). The IMU-based joint angle estimation (JAE) is a typical usage of IMUs for WSN, with extensive applications. However, the issue that IMUs move with respect to their original placement during JAE is still a research gap and limits the robustness of deploying the technique in real-world application scenarios. In this study, we propose to detect and correct the IMU movement online in a computationally lightweight manner. In particular, we first experimentally investigate the influence of IMU movements. Second, we design the metrics for detecting IMU movements by mathematically formulating how the IMU movement affects the IMU’s measurements. Third, we determine the optimal thresholds of metrics by synthetic IMU data from a significantly amended simulation model. Finally, a correction method is proposed to correct the effects of IMU movements. We demonstrate our method on both synthetic data and real-user data. Our proposed method achieves a detection rate of 94.8%, a misdetection rate of 9.8%, and the calculation time of 2.15 ms for detecting IMU movements and can restore the accuracy of JAE. All of the results significantly outperform the intuitive method. The results demonstrate our method is a promising solution to detect and correct IMU movements during JAE. This study will aid the deployment of IMU-based JAE in real-world applications.