Brain-computer interface (BCI) provides new communication and control channels that do not depend on the brain’s normal output of peripheral nerves and muscles. In this paper, we report on results of developing a single trial online motor imagery feature extraction method for BCI. The wavelet coefficients and autoregressive parameter model was used to extraction the features from the motor imagery EEG and the linear discriminant analysis based on mahalanobis distance was utilized to classify the pattern of left and right hand movement imagery. The performance was tested by the Graz dataset for BCI competition 2003 and satisfactory results are obtained with an error rate as low as 10.0%.
Upper limb rehabilitation requires long-term, repetitive rehabilitation training and assessment. However, many patients cannot afford for heavy medical fees. It is necessary to design an effective, low-cost, and reasonable home rehabilitation and evaluation system. In this paper, we developed a novel home-based multi-scene upper limb rehabilitation training and evaluation system for post-stroke patients. Based on the Kinect sensor and the posture sensor, the multi-sensors fusion method was used to track the motion of the patients. Multiple virtual scenes were designed to encourage rehabilitation training of upper limbs and trunk. A rehabilitation evaluation method was proposed integrating Fugl-Meyer assessment (FMA) scale and upper limb reachable workspace relative surface area (RSA). Furthermore, an FMA-RSA assessment model was established to assess an upper limb motor function. Correlation-based dynamic time warping was used to solve the problem of inconsistent upper limb movement path in different patients. Two clinical trials were conducted. The experimental results show that the system is very friendly to the subjects. The rehabilitation assessment results by this system are highly correlated with the therapist's (the highest forecast accuracy was 92.7% in the 13th item). It also reveals that long-term rehabilitation training can improve the upper limb motor function of the patients statistically significant (p=0.02 <; 0.05). The system has the potential to become an effective home rehabilitation training and evaluation system.
This paper addresses the problem of motion prediction and tracking control for cloud robotic systems with time-varying delays in measurements. A novel method using an observer-based structure for position and velocity prediction is developed to estimate the real-time information of robot manipulator. The prediction error can converge to zero even if model uncertainties exist in the robot manipulator. Based on the predicted positions and velocities, some sufficient conditions are derived to design suitable tracking controllers such that semi-globally uniformly ultimately bounded tracking performance of the predictor–controller couple can be guaranteed. Finally, the effectiveness and robustness to model uncertainties of the proposed method are verified by a two degree-of-freedom (DOF) robot system.
This paper presents a deployable boom which combines a rigid telescopic frame and a flexible tape spring. The front end of the spring is fixed on the rear end of the innermost segment of the frame. The spring spreads and rolls up inside the frame to drive the segments to move one by one to realize the boom deployment and retraction. The driving forces needed to deploy and retract the frame are modeled and simulated. The feasibility of the frame driving by only one spring is also studied. A 1.6 kg prototype system with 2.1 m total deployment length is implemented. Experimental results show the maximum driving forces for deployment and retraction of the frame are about 9.1 N and 6.8 N respectively. The boom is able to deploy in 76 s with energy consumption of 315 J. The boom can resist at least 15 N force axially and 31.5 N·m bending moment when the forces are acted on its front end. Advantages of this kind of boom enable it to be applied for instruments deployment, walking and sampling assists, and robotic arms design in space exploration.
Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closed-loop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closed-loop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phase-dependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closed-loop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.
As an important component of force feedback device, multi-dimensional force sensor has been widely used in haptic device, prosthetic hand and other devices. In a given application, the multi-dimensional force sensor is always coupled with a load by the screw thread, which may result in a totally different dynamic behavior from a bare force sensor. It is the key data for deeply understanding the dynamic performance to have information about the structural distribution of stiffness and mass of the multi-dimensional force sensor. To address this need, the approach of model-based load characteristic analysis of the multi-dimensional force sensor is presented in this paper. Dynamic behavior of the force sensor in a given load is described by a lumped mass model consists of spring-mass-damper elements and characterized by the model parameters that describe the dynamic correlation distribution of mass, stiffness, and damping. The main purpose of the proposed approach is the description of the load characteristics of the multi-dimensional force sensor, independent of the given mechanical environment, which provides a certain theoretical reference for the calculation of the load capacity of the force sensor.
The efficient integration of visual and tactile information is essential for slip detection and evaluation of grasping stability. However, existing research generally combines visual or tactile modalities as prior information, without fully exploring mechanisms for fusing complementary modalities. In this article, we propose a visual–tactile fusion Transformer (VTF-Trans) for slip detection, designed to handle unaligned data in different formats and facilitate cross-modal information exchange. The main advantages of the proposed method are summarized as follows: first, VTF-Trans employs an improved dual-stream transformer for feature extraction. In addition, we introduce a gated modal attention module to further refine cross-modal fusion. Compared with the existing methods, VTF-Trans effectively integrates useful information from different modalities across multiple scales. Second, to extract deep multimodal information, we propose a cross-modal attention (CMA) mechanism. By defining cross-affinity based on single-modality affinities (token metrics), CMA naturally alleviates the gap between different modalities and domains. Third, we evaluate VTF-trans on three datasets and conduct unknown object grasping experiments. Compared with the state-of-the-art methods, VTF-Trans achieves the highest accuracy for robotic grasping and slip detection, highlighting its superior performance and practical applicability.
Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.
The Chang'E-1 Laser Altimeter(LAM), as one of the scientific instruments onboard the Chinese Chang'E-1 orbiter, has successfully gained the massive lunar elevation scientific data of global topography of the moon. Uncertainty evaluation of the lunar elevation detection error based on LAM scientific data is developed in this paper. Firstly, the data are selected from the flat terrain region in all the lunar elevation detection data; Secondly, after the pseudo elevation data are removed in the selected region, regional elevation mean and standard deviation are calculated. Making use of the calculations and taking into account all kinds of uncertainty contributors of LAM orbiting exploring, the uncertainty evaluation methods of the LAM in-orbit elevation exploring are proposed on the basis of the guide to Monte Carlo Methods. Finally, the uncertainty evaluation results of different regions of lunar surface are given. The evaluation results not only can provide the basis for further analysis laser altimeter measurement error sources, but also give the reference for making the high precision moon digital elevation graph and provide theoretical guidance for the accuracy requirement of design of payload on lunar orbiter.
