Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

Objective. In order to study the motion recognition intention of lower limb prosthesis based on the CNN deep learning algorithm. Methods. A convolutional neural network (CNN) model was established to reconstruct the motion pattern. Before the movement mode of the affected side was converted, the sensor was bound to the healthy side. The classifier was employed to extract and classify the features, so as to realize the accurate description of the movement intention of the disabled. Results. The method proposed in this research can achieve 98.2% recognition rate of the movement intention of patients with lower limb amputation under different terrains, and the recognition rate can reach 97% after the pattern converted between the five modes was added. Conclusion. The deep learning algorithm that automatically recognized and extracted features can effectively improve the control performance on the intelligent lower limb prosthesis and realize the natural and seamless conversion of the intelligent prosthesis in a variety of motion modes.

Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

Patients with motor impairments need caregivers’ help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

Towards Transparent Motion Planning of Wearable Rehabilitation Exoskeletons via Model-Based Estimation

We propose a novel online model-based motion planning algorithm for a family of rehabilitation exoskeletons to improve transparency in user-guided operation. In this study, we assume that the short-term human movement intention can be embedded in the time-delay dimensions of motion signals. The model-based estimation is employed to obtain the interaction load between the dynamical subsystems respectively controlled by the human and exoskeleton. The objective of the proposed motion planning algorithm is to reduce the interaction load, which leads to the establishment of a least-square optimization problem. A Support Vector Regression (SVR) model, driven by the time-delayed motion data, is implemented to solve the optimization problem by generating the acceleration of tracking reference. The motion planning algorithm based on SVR can be combined with a variety of trajectory tracking controllers. To ensure the efficiency of the algorithm for online applications, we also design the SVR model so that its properties can be calculated recursively based on latest data sets. The performance and characteristics of the motion planning algorithm are then observed and discussed through the control simulations of a wearable wrist exoskeleton designed for pathological tremor alleviation. The results show that while the planned tracking reference can approximate the synthetic human movement intention, the motion planning accuracy can be limited by system disturbances, and the delay of signals caused by digital filters.

Adults Do Not Distinguish Action Intentions Based on Movement Kinematics Presented in Naturalistic Settings

Predicting others’ actions is an essential part of acting in the social world. Action kinematics have been proposed to be a cue about others’ intentions. It is still an open question as to whether adults can use kinematic information in naturalistic settings when presented as a part of a richer visual scene than previously examined. We investigated adults’ intention perceptions from kinematics using naturalistic stimuli in two experiments. In experiment 1, thirty participants watched grasp-to-drink and grasp-to-place movements and identified the movement intention (to drink or to place), whilst their mouth-opening muscle activity was measured with electromyography (EMG) to examine participants’ motor simulation of the observed actions. We found anecdotal evidence that participants could correctly identify the intentions from the action kinematics, although we found no evidence for increased activation of their mylohyoid muscle during the observation of grasp-to-drink compared to grasp-to-place actions. In pre-registered experiment 2, fifty participants completed the same task online. With the increased statistical power, we found strong evidence that participants were not able to discriminate intentions based on movement kinematics. Together, our findings suggest that the role of action kinematics in intention perception is more complex than previously assumed. Although previous research indicates that under certain circumstances observers can perceive and act upon intention-specific kinematic information, perceptual differences in everyday scenes or the observers’ ability to use kinematic information in more naturalistic scenes seems limited.

Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. The mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analyze the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.

Research on fNIRS Recognition Method of Upper Limb Movement Intention

This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.