Artificial prosthesis is an important tool to help amputees to gain or partially obtain abled human limb functions. Compared with traditional prosthesis which is only for decoration or merely has feedforward control channel, the perception and feedback function of prosthesis is an important guarantee for its normal use and self-safety. And this includes the information of position, force, texture, roughness, temperature and so on. This paper mainly summarizes the development and current status of artificial prostheses in the field of perception and feedback technology in recent years, which is derived from two aspects: the recognition way of perception signals and the feedback way of perception signals. Among the part of recognition way of perception signals, the current commonly adopted sensors related to perception information acquisition and their application status in prosthesis are overviewed. Additionally, from the aspects of force feedback stimulation, invasive/non-invasive electrical stimulation, and vibration stimulation, the feedback methods of perception signals are summarized and analyzed. Finally, some problems existing in the perception and feedback technology of artificial prosthesis are proposed, and their development trends are also prospected.
With the advantage of providing more natural and flexible control manner, brain-computer interface systems based on motor imagery electroencephalogram (EEG) have been widely used in the field of human-machine interaction. However, due to the lower signal-noise ratio and poor spatial resolution of EEG signals, the decoding accuracy is relative low. To solve this problem, a novel convolutional neural network based on temporal-spatial feature learning (TSCNN) was proposed for motor imagery EEG decoding. Firstly, for the EEG signals preprocessed by band-pass filtering, a temporal-wise convolution layer and a spatial-wise convolution layer were respectively designed, and temporal-spatial features of motor imagery EEG were constructed. Then, 2-layer two-dimensional convolutional structures were adopted to learn abstract features from the raw temporal-spatial features. Finally, the softmax layer combined with the fully connected layer were used to perform decoding task from the extracted abstract features. The experimental results of the proposed method on the open dataset showed that the average decoding accuracy was 80.09%, which is approximately 13.75% and 10.99% higher than that of the state-of-the-art common spatial pattern (CSP) + support vector machine (SVM) and filter bank CSP (FBCSP) + SVM recognition methods, respectively. This demonstrates that the proposed method can significantly improve the reliability of motor imagery EEG decoding.
Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.
In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients (r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of “human in the loop” and provides a potential method for remote rehabilitation training and assessment of the lower extremity.