Xiangyang Zhu

Synthesis of force-closure grasps on 3-D objects based on the Q distance

The synthesis of force-closure grasps on three-dimensional (3-D) objects is a fundamental issue in robotic grasping and dextrous manipulation. In this paper, a numerical force-closure test is developed based on the concept of Q distance. With some mild and realistic assumptions, the proposed test criterion is differentiable almost everywhere and its derivative can be calculated exactly. On this basis, we present an algorithm for planning force-closure grasps, which is implemented by applying descent search to the proposed numerical test in the grasp configuration space. The algorithm is generally applicable to planning optimal force-closure grasps on 3-D objects with curved surfaces and with arbitrary number of contact points. The effectiveness and efficiency of the algorithm are demonstrated by using simulation examples.

Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control.

BackgroundThe nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset.MethodThis paper presents a novel self-enhancing approach to improve the classification performance of the electromyography (EMG) pattern recognition (PR). The proposed self-enhancing method incorporates the knowledge beyond the training condition to the classifiers from the testing data. The widely-used linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are extended to self-enhancing LDA (SELDA) and self-enhancing QDA (SEQDA) by continuously updating their model parameters such as the class mean vectors, the class covariances and the pooled covariance. Autoregressive (AR) and Fourier-derived cepstral (FC) features are adopted. Experimental data in two different protocols are used to evaluate performance of the proposed methods in short-term and long-term application respectively.ResultsIn protocol of short-term EMG, based on AR and FC, the recognition accuracy of SEQDA and SELDA is 2.2% and 1.6% higher than conventional that of QDA and LDA respectively. The mean results of SEQDA(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for FC respectively when compared to QDA. The mean results of SELDA(C) and SELDA (M) are improved by 0.48% and 1.55% for AR, and 0.67% and 1.22% for FC when compared to LDA. In protocol of long-term EMG, the mean result of SEQDA is 3.15% better than that of QDA.ConclusionThe experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA.

Grasp analysis and synthesis based on a new quantitative measure

In this paper, we present a quantitative measure of multifingered grasps. The measure quantifies the capability of a grasp in firmly holding an object while resisting external loads and/or disturbances. It can also be used for qualitative test of closure properties (form closure and force closure). For planar grasps and frictionless three-dimensional (3-D) grasps, the quantitative measure can be computed efficiently by solving a set of linear programs, while for frictional 3-D grasps, it can be computed by solving nonlinear programs without linearization of the friction cone. By using the proposed quantitative measure, an algorithm for grasp synthesis on polygonal objects is developed. Rather than producing a single grasp configuration, the algorithm computes all grasps on a polygon that satisfy quantitative constraints, i.e., the value of the quantitative measure is greater than a predetermined positive constant. The approach has potential application in grasp planning with multiple optimality criteria.

A discriminant bispectrum feature for surface electromyogram signal classification

This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies-Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%.

Invariant Surface EMG Feature Against Varying Contraction Level for Myoelectric Control Based on Muscle Coordination

Variations in muscle contraction effort have a substantial impact on performance of pattern recognition based myoelectric control. Though incorporating changes into training phase could decrease the effect, the training time would be increased and the clinical viability would be limited. The modulation of force relies on the coordination of multiple muscles, which provides a possibility to classify motions with different forces without adding extra training samples. This study explores the property of muscle coordination in the frequency domain and found that the orientation of muscle activation pattern vector of the frequency band is similar for the same motion with different force levels. Two novel features based on discrete Fourier transform and muscle coordination were proposed subsequently, and the classification accuracy was increased by around 11% compared to the traditional time domain feature sets when classifying nine classes of motions with three different force levels. Further analysis found that both features decreased the difference among different forces of the same motion p <; 0.005) and maintained the distance among different motions p > 0.1). This study also provided a potential way for simultaneous classification of hand motions and forces without training at all force levels.

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

OBJECTIVE Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. APPROACH In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. MAIN RESULTS It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. SIGNIFICANCE These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., the classification between left/ right stimulation sensation and right/ left motor imagery. In this paradigm, wearable vibrotactile rings are used to stimulate both the skin on both wrists. Subjects are required to perform the mental tasks according to the randomly presented cues (i.e., left hand motor imagery, right hand motor imagery, left stimulation sensation or right stimulation sensation). Two-way ANOVA statistical analysis showed a significant group effect (F (2,20) = 7.17, p = 0.0045), and the Benferroni-corrected multiple comparison test (with α = 0.05) showed that the hybrid modality group is 11.13% higher on average than the motor imagery group, and 10.45% higher than the selective sensation group. The hybrid modality experiment exhibits potentially wider spread usage within ten subjects crossed 70% accuracy, followed by four subjects in motor imagery and five subjects in selective sensation. Six subjects showed statistically significant improvement ( Benferroni-corrected) in hybrid modality in comparison with both motor imagery and selective sensation. Furthermore, among subjects having difficulties in both motor imagery and selective sensation, the hybrid modality improves their performance to 90% accuracy. The proposed hybrid modality BCI has demonstrated clear benefits for those poorly performing BCI users. Not only does the requirement of motor and sensory anticipation in this hybrid modality provide basic function of BCI for communication and control, it also has the potential for enhancing the rehabilitation during motor recovery.

Planning force-closure grasps on 3-D objects

Computation of grasps with form/force-closure is one of the fundamental problems in the study of multifingered grasping and dexterous manipulation. Based on the geometric condition of the closure property, this paper presents a numerical test to quantify how far a grasp is from losing form/force-closure. With the polyhedral approximation of the friction cone, the proposed numerical test can be formulated as a single linear program. An iterative algorithm for computing optimal force-closure grasps, which is implemented by minimizing the proposed numerical test in the grasp configuration space, is also developed. The algorithm is computationally efficient and generally applicable. It can be used for computing form/force-closure grasps on 3D objects with curved surfaces, and with any number of contact points. Several simulation examples are given to show the effectiveness and computational efficiency of the proposed algorithm.

A pseudodistance function and its applications

By using the concept of gauge function, a pseudodistance function is defined for quantifying the clearance or the penetration depth of two convex point sets, depending on whether they separate or intersect. The linear programming formulations (for convex polyhedra) and the nonlinear constrained optimization formulations (for general convex objects) are presented for its calculation. For a pair of convex polyhedra, the pseudodistance function is differentiable almost everywhere with respect to the coordinate vectors of their vertices. Sufficient conditions for the differentiability and the characterization of its derivative are presented. By applying the pseudodistance function to the wrench space, a numerical measure of multifingered grasps is defined, which can be used for qualitative test and quantitative analysis of the force-closure property. On this basis, two algorithms for planning optimal force-closure grasps on general three-dimensional objects are developed. In addition, the application of the pseudodistance function in robot path planning is also demonstrated.

A numerical test for the closure properties of 3-D grasps

This paper presents a numerical test for the closure properties (force closure and form closure) of multifingered grasps. For three-dimensional (3-D) grasps with frictional point contacts or soft contacts, the numerical test is formulated as a convex constrained optimization problem without linearization of the friction cone. For 3-D frictionless grasps, it can be calculated by solving a single linear program. The proposed numerical test (along with the rank of the grasp matrix) provides an efficient tool for the analysis of the force-closure property and the relative force-closure property.