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Recent advances on human motion analysis have made the extraction of human skeleton structure feasible, even from single depth images. This structure has been proven quite informative for discriminating actions in a recognition scenario. In this context, we propose a local skeleton descriptor that encodes the relative position of joint quadruples. Such a coding implies a similarity normalisation transform...
This paper aims to reconstruct retinal vessel trees from the broken vessel segments in fund us images for clinical studies and early diagnosis of systemic diseases including diabetic retinopathy, atherosclerosis, and hypertension. A Naive Bayes model is proposed for correct configurations of segments at retinal junctions including bifurcations, crossovers, overlaps, and mixture of these. The Maximum...
This paper presents a multi-joint lower limbs rehabilitation robot with three degrees of freedom. The robot includes seat, left mechanical leg, right mechanical leg and electric control box, and each mechanical leg includes the hip joint, knee joint and ankle joint which correspond to the hip joint, knee joint and ankle joint of human. The mechanical structure of the rehabilitation robot is described...
In this paper we introduce a novel method for movement recognition in motion capture data. A movement is regarded as a combination of basic movement patterns, the so-called dynemes. Initially a K-means variant that takes into account the periodic nature of angular data is applied on training data to discover the most discriminative dynemes. Each frame is then assigned to one of these dynemes and a...
Gesture recognition using RGB-D sensors has currently an important role in many fields such as human-computer interfaces, robotics control, and sign language recognition. However, the recognition of hand gestures under natural conditions with low spatial resolution and strong motion blur still remains an open research question. In this paper we propose an online gesture recognition method for multimodal...
In this paper, a novel method is presented for low-latency online action recognition from skeleton data. The introduction of pose based features has reduced viewpoint and anthropometric variations, so differing execution rates and personal styles are the major sources of classification error. Previous work for online action recognition fails to adequately address both execution rate and personal style...
With the introduction of low cost depth sensors, gesture recognition systems for computer interface control are becoming a reality and require accurate recognition in real time. In this paper, we introduce a salient feature for gesture recognition called active difference signature, obtained by robust processing of depth maps and kinematic joint information. This feature is classified with variants...
This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into...
Object Classification in traffic scene surveillance has gained popularity in recent years. Traditional methods tend to utilize a large number of labeled training samples to achieve a satisfactory classification performance. However, labels of samples are not always available and manual labeling work is both time and labor consuming. To address the problem, a large number of semi-supervised learning...
Most current approaches in action recognition face difficulties that cannot handle recognition of multiple actions, fusion of multiple features, and recognition of action in frame by frame model, incremental learning of new action samples and application of position information of space-time interest points to improve performance simultaneously. In this paper, we propose a novel approach based on...
In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn...
Recent research has demonstrated that sparse coding (or sparse representation) is a powerful tool for pattern classification. This paper presents a new unsupervised feature selection method, termed Sparse Representation Preserving Feature Selection (SRPFS), which aims at minimizing reconstruction residual based on sparse representation in the subspace of the selected features. A greedy algorithm and...
A portable rehabilitation robot incorporating intelligent stretching, robot-guided voluntary movement training with motivating games and tele-rehabilitation was developed to provide convenient and cost-effective rehabilitation to children with cerebral palsy (CP) and extend rehabilitation care beyond hospital. Clinicians interact with the patients remotely for periodic evaluations and updated guidance...
Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches...
Quantitative analysis of surgeons' motor variability during the surgical practice is still scarce. Therefore, a framework for the analysis of surgeon upper-body postural variability during laparoscopic procedures was developed. 3D kinematics analysis gave us information regarding the head posture adopted by the surgeons with respect to the trunk and how this varies during surgical training activities...
A mechatronic system for neurorehabilitation of motion system of the human lower limbs is presented. Moreover, the structure of the complex and its components — feet training device with acupressure effect on feet, half-bed standing frame (verticalizer), lower limbs exoskeleton to operate them in case of loss of mobility or for active workouts are presented. The complex is designed to help patients...
The tacit representation of muscle coordination has been a major topic of research on motor control since Bernstein's pioneering work. To unravel the mechanisms underlying voluntary movements, we investigated the electromyography signals of six muscles in a non-dominant upper limb during fast spiral movements on a horizontal plane. We considered muscle synergy to be a coordination index that we defined...
In this paper, classification via joint sparse representation of the monogenic signal is presented for target recognition in SAR imagery. First, the monogenic signal is performed to capture the characteristics of SAR image. Since it is infeasible to directly apply the raw component to classification due to the high data dimension and redundancy, three augmented feature vectors are defined via uniform...
Existing exoskeletons for rehabilitation usually consist of a rigid kinematic chain of various forms. They are often bulky in size and need structural adjustments from time to time to fit to a specific patient. This paper presents a continuum exoskeleton design for bilateral rehabilitation. Its intrinsic flexibility adapts to different patient anatomies passively to provide AAA (Anatomy Adaptive Assistances)...
Object tracking is a widely researched topic with applications in event detection, surveillance and behavior analysis. There are three key steps in object tracking: feature extraction, deformation handling, and classification. In this paper, we present a joint method combining feature and deformation handling with classification model for object tracking. Multi-scale tracking map are obtained from...
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