91 resultados para human-action recognition


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In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics.

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This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

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Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

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The authors are concerned with the development of computer systems that are capable of using information from faces and voices to recognise people's emotions in real-life situations. The paper addresses the nature of the challenges that lie ahead, and provides an assessment of the progress that has been made in the areas of signal processing and analysis techniques (with regard to speech and face), and the psychological and linguistic analyses of emotion. Ongoing developmental work by the authors in each of these areas is described.

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T cell immune responses to central nervous system-derived and other self-antigens are commonly described in both healthy and autoimmune individuals. However, in the case of the human prion protein (PrP), it has been argued that immunologic tolerance is uncommonly robust. Although development of an effective vaccine for prion disease requires breaking of tolerance to PrP, the extent of immune tolerance to PrP and the identity of immunodominant regions of the protein have not previously been determined in humans. We analyzed PrP T cell epitopes both by using a predictive algorithm and by measuring functional immune responses from healthy donors. Interestingly, clusters of epitopes were focused around the area of the polymorphic residue 129, previously identified as an indicator of susceptibility to prion disease, and in the C-terminal region. Moreover, responses were seen to PrP peptide 121-134 containing methionine at position 129, whereas PrP 121-134 [129V] was not immunogenic. The residue 129 polymorphism was also associated with distinct patterns of cytokine response: PrP 128-141 [129M] inducing IL-4 and IL-6 production, which was not seen in response to PrP 128-141 [129V]. Our data suggest that the immunogenic regions of human PrP lie between residue 107 and the C-terminus and that, like with many other central nervous system antigens, healthy individuals carry responses to PrP within the T cell repertoire and yet do not experience deleterious autoimmune reactions.

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In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

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DNA-dependent protein kinase (DNA-PK) has been implicated in a variety of nuclear processes including DNA double strand break repair, V(D)J recombination, and transcription. A recent study showed that DNA-PK is responsible for Ser-473 phosphorylation in the hydrophobic motif of protein kinase B (PKB/Akt) in genotoxic-stressed cells, suggesting a novel role for DNA-PK in cell signaling. Here, we report that DNA-PK activity toward PKB peptides is impaired in DNA-PK knock-out mouse embryonic fibroblast cells when compared with wild type. In addition, human glioblastoma cells expressing a mutant form of DNA-PK (M059J) displayed a lower DNA-PK activity when compared with glioblastoma cells expressing wild-type DNA- PK (M059K) when PKB peptide substrates were tested. DNA- PK preferentially phosphorylated PKB on Ser-473 when compared with its known in vitro substrate, p53. A consensus hydrophobic amino acid surrounding the Ser-473 phospho-acceptor site in PKB containing amino acids Phe at position +1 and +4 and Tyr at position -1 are critical for DNA- PK activity. Thus, these data define the specificity of DNA- PK action as a Ser-473 kinase for PKB in DNA repair signaling.

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Although the incretin hormone glucagon-like peptide-1 (GLP-1) is a potent stimulator of insulin release, its rapid degradation in vivo by the enzyme dipeptidyl peptidase IV (DPP IV) greatly limits its potential for treatment of type 2 diabetes. Here, we report two novel Ala(8)-substituted analogues of GLP-1, (Abu(8))GLP-1 and (Val(8) GLP-1 which were completely resistant to inactivation by DPP IV or human plasma. (Abu(8))GLP-1 and (Val(8))GLP-1 exhibited moderate affinities (IC50: 4.76 and 81.1 nM, respectively) for the human GLP-1 receptor compared with native GLP-1 (IC50: 0.37 nM). (Abu(8))GLP-1 and (Val(8))GLP-1 dose-dependently stimulated cAMP in insulin-secreting BRIN BD11 cells with reduced potency compared with native GLP-1 (1.5- and 3.5-fold, respectively). Consistent with other mechanisms of action, the analogues showed similar, or in the case of (Val(8))GLP-1 slightly impaired insulin releasing activity in BRIN BD11 cells. Using adult obese (ob/ob) mice, (Abu(8))GLP-1 had similar glucose-lowering potency to native GLP-1 whereas the action of (Val(8))GLP-1 was enhanced by 37%. The in vivo insulin-releasing activities were similar. These data indicate that substitution of Ala(8) in GLP-1 with Abu or Val confers resistance to DPP IV inactivation and that (Val(8))GLP-1 is a particularly potent N-terminally modified GLP-1 analogue of possible use in type 2 diabetes.