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This paper aims at dynamic modeling and control of a new upper-limb rehabilitation robot which has a parallel structure. Dynamic modeling of parallel robot is a complicated problem, and the dynamics and voluntary force of the patient arm increase the difficulty of dynamic analysis and control in rehabilitation training. The novelties of this study are: (1) dynamics of the robot and the patient are...
Poor performance while using a prosthesis can stress the joints excessively and lead to chronic injuries or rejection of the prosthesis. In order to improve upper limb prosthetic performance, compensatory motion of amputees is often compared with the movements of able-bodied persons. This technique does not consider the limitations of the degrees of freedom (DoF) of a prosthesis. This paper presents...
We have developed a real-time machine learning approach for the collaborative control of a prosthetic arm. Upper-limb amputees are often extremely limited in the number of inputs they can provide to their prosthetic device, typically controlling only one joint at a time with the ability to toggle their control between the different joints of their prosthesis. Many users therefore consider the control...
Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few years. In this paper, we have presented a Gait based human identification system using skeleton data acquired by using Microsoft Kinect sensor. The sensor acts as a digital eye which takes...
The recovery of motor function after stroke is widely considered to result from brain plasticity. However, what kind of training exercise can better provoke brain plastic processes is still unclear. Studying regional brain activation during a specific training exercise may provide value information that can help design more effective therapeutic approaches. In this paper, we monitored brain activation...
This paper describes the development of an ankle training robot force visualization for eccentric contraction training. In the mechanism of walking, the eccentric contraction of the tibialis anterior muscle plays an important part in the phase between heel-strike and foot-flat as a shock absorber. However, the eccentric contraction has not been reported in previous research on ankle rehabilitation...
Movement prediction is a key ingredient in exoskeleton robot control for walking assistance. In this paper, we propose a movement prediction method with following two desirable fundamental properties: 1) fast online calibration for a novel user, and 2) applicability to partially observable situations. Using this method, for example, 1) we can use previously collected other subjects' walking data to...
This paper evaluates the use of Gaussian Mixture Model (GMM) trained through Electromyography (EMG) signals to online estimate the bending angle of a single human joint. The parameters involved in the evaluation are the number of Gaussian components, the channel used for model, the feature extraction method, and the size of the training set. The feature extraction is performed through Wavelet Transform...
In this paper, we present a new semi-supervised method for the classification of hyperspectral and VHR remote sensing images. The method is based on a hierarchical learning paradigm which is composed of multiple layers feeding into each other: 1) feature extraction layer, 2) classification layer, and 3) spatial regularization layer. In the feature extraction layer, the method employs morphological...
Task-driven dictionary learning (TDDL) has shown great success in many classification applications. However, the performance of TDDL is limited by the challenging properties of hyperspectral images (HSI). Fortunately, previous research has made significant progress in HSI classification by enforcing various structured sparsity constraints (priors) on the TDDL-based model. In this paper, we extend...
With regard to the specific role of each pixel within a spatial parcel of a hyperspectral image (HSI), we propose a novel superpixel-oriented sparse representation classification method with a multi-task learning approach. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and also the correlation and distinctiveness of pixels in a spatial local region. Compared...
The karate movements classification is extremely challenging task due to the speed of body movements. From the other hand movements patterns are highly repetitive because they are practiced for many years by skilled martial artists. Those two facts make karate techniques classification tasks reliable tests of classifiers potential. Also, nowadays there is a growing interest on commercial market for...
In this paper, a novel nonlocal dictionary learning method is proposed for sparse-representation-based classification (SRC) to label high-dimensional hyperspectral imagery (HSI). In SRC, the conventional dictionary is constructed using all of the training pixels, which is inefficient due to the high-dimension low-sample-size classification problem. In this paper, we construct the dictionary by adding...
In this paper, rehabilitation aid system by selectable DOF constraintable mechanism and NMES (Neuromuscular Electrical Stimulation) for hemiplegic upper limbs was developed. By using this mechanism, it became possible to separate synergic movement while flexion-extension training of shoulder and elbow by constraining each individual joints. As the clinical trial result by using this mechanism and...
We present a digital algorithm for joint pre-compensation of the low-pass frequency response and I/Q skew in transmitters. Experimental results for DP-16QAM to DP-256QAM at 37.41 GBaud are presented.
We address the task of articulated pose estimation from video sequences. We consider an interactive setting where the initial pose is annotated in the first frame. Our system synthesizes a large number of hypothetical scenes with different poses and camera positions by applying geometric deformations to the first frame. We use these synthetic images to generate a custom labeled training set for the...
When learning a new classifier, poor quality training data can significantly degrade performance. Applying selection conditions to the training data can prevent mislabeled, noisy, or damaged data from skewing the classifier. We extend a set of action attributes and apply training case attribute selection conditions to a challenging action recognition dataset.
A lot of real-world data is spread across multiple domains. Handling such data has been a challenging task. Heterogeneous face biometrics has begun to receive attention in recent years. In real-world scenarios, many surveillance cameras capture data in the NIR (near infrared) spectrum. However, most datasets accessible to law enforcement have been collected in the VIS (visible light) domain. Thus,...
Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where...
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relatively speaking, not affected by over-fitting or over-smoothing problems. In these approaches, voice conversion is achieved by estimating a sparse vector that determines which dictionaries of the target speaker should be used, calculated from the matching of the input vector and dictionaries...
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