2,068 research outputs found

    One-shot action recognition in challenging therapy scenarios

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    One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy. © 2021 IEEE

    Learning Deep Features for Robotic Inference from Physical Interactions

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    In order to effectively handle multiple tasks that are not pre-defined, a robotic agent needs to automatically map its high-dimensional sensory inputs into useful features. As a solution, feature learning has empirically shown substantial improvements in obtaining representations that are generalizable to different tasks, compared to feature engineering approaches, but it requires a large amount of data and computational capacity. These challenges are specifically relevant in robotics due to the low signal-to-noise ratios inherent to robotic data, and to the cost typically associated with collecting this type of input. In this paper, we propose a deep probabilistic method based on Convolutional Variational Auto-Encoders (CVAEs) to learn visual features suitable for interaction and recognition tasks. We run our experiments on a self-supervised robotic sensorimotor dataset. Our data was acquired with the iCub humanoid and is based on a standard object collection, thus being readily extensible. We evaluated the learned features in terms of usability for 1) object recognition, 2) capturing the statistics of the effects, and 3) planning. In addition, where applicable, we compared the performance of the proposed architecture with other state-ofthe-art models. These experiments demonstrate that our model is capable of capturing the functional statistics of action and perception (i.e. images) which performs better than existing baselines, without requiring millions of samples or any handengineered features

    Affordances in Psychology, Neuroscience, and Robotics: A Survey

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    The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics

    Um método proativo para gerenciamento da segurança em instalações nucleares

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    Em razão da abordagem moderna para tratar a segurança em instalações nucleares que destaca que estas organizações devem ser capazes de avaliar e gerenciar de forma proativa suas atividades torna-se cada vez mais importante a necessidade de instrumentos de avaliação das condições de trabalho. Nesse contexto, este trabalho apresenta um método proativo de gerenciamento da segurança organizacional, o qual apresenta três características inovadoras: 1) a utilização de indicadores preditivos que fornecem informações atuais sobre o desempenho das atividades, permitindo ações preventivas e não somente reativas na gestão da segurança, diferente dos indicadores de segurança tradicionalmente utilizados (indicadores reativos) que são obtidos após a ocorrência de eventos indesejados; 2) a adoção do enfoque da engenharia de resiliência no desenvolvimento dos indicadores – os indicadores são baseados em seis princípios da engenharia de resiliência: comprometimento da alta direção, aprendizagem, flexibilidade, consciência, cultura de justiça e preparação para os problemas; 3) a adoção dos conceitos e propriedades da teoria dos conjuntos fuzzy para lidar com a subjetividade e a consistência dos julgamentos humanos na avaliação dos indicadores. A teoria fuzzy é usada essencialmente para mapear modelos qualitativos de tomada de decisão, e para métodos de representação imprecisa. Os resultados deste trabalho objetivam uma melhoria no desempenho e na segurança nas organizações. O método foi aplicado no setor de expedição de radiofármacos de uma instalação nuclear. Os resultados mostraram que o método é uma boa ferramenta de monitoramento de forma objetiva e proativa das condições de trabalho de um domínio organizacional

    A METHOD FOR AUTOMATIC RELOCATION OF SKIN MARKERS IN REARFOOT MOTION ANALYSIS

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    INTRODUCTION: The authors are collaborating in a project aiming at developing computer based strategies to improve the analysis of the visual and ground reaction forces signal during passive or active foot load. The main objectives of the project are the automatic detection of the skin markers in image sequences for kinematics analysis, and the synchronisation the visual and ground reaction force data acquisition systems to achieve dynamic joint measurements. In this paper, a method for automatic relocation of skin markers in rearfoot motion analysis is proposed. The results of preliminary tests are critically described on full paper. METHODS: The visual signal is acquired using a high-speed digital camera (DALSA CA-D1), at 225 frames per second. A prototype system is being built, based on an IBM-PC running the Windows NT operating system. A windows interface has been developed, in order to facilitate data analysis. Data can be exported to a spreadsheet or to a statistical package for further processing. The experiments conduced focused on the observation of the ankle joint complex kinematics behaviour on the posterior aspect of the frontal plane during stance phase of walking barefoot. The skin markers have been clearly marked on the rear part of the leg and on the heel (black ink on a white stripe of tape). These markers are localised in the computer screen by hand, using the mouse, in the first frame of the sequence. The markers are automatically located in the other image frames of the sequence, using the proposed method, based on optimised block-matching. The space calibration procedure uses a scale marked by two targets located perpendicularly on the camera axis. These two points mark the horizontal. Comparison between the real measures of one well know rigid body and measures calculated trough the computer helps preventing some misbehaviour of the automatic analysis. RESULTS: The prototype system was applied to recorded data of angular motion of the ankle joint complex during two walking tasks. Our detailed motion analysis scheme during these motor tasks was successful in using the proposed automatic relocation method, with great economy to the operator. CONCLUSION: This automatic localisation solution represents a step towards the analysis of sequences acquired with high frame rates, justified by the fact that image acquisition with standard equipment (25 or 30 frames per second) presents strong aliasing problems. The method presented here is expected to be a useful tool for other kinematics behaviour studies

    Benchmarking the Grasping Capabilities of the iCub Hand with the YCB Object and Model Set

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    © 2016 IEEE. The letter reports an evaluation of the iCub grasping capabilities, performed using the YCB Object and Model Set. The goal is to understand what kind of objects the iCub dexterous hand can grasp, and with what degree of robustness and flexibility, given the best possible control strategy. Therefore, the robot fingers are directly controlled by a human expert using a dataglove: in other words, the human brain is employed as the best possible controller. Through this technique, we provide a baseline for researchers who want to evaluate the performance of their grasping controller. By using a widespread robotic platform and a publicly available set of objects, we believe that many researchers can directly benefit from this resource; moreover, what we propose is a general methodology for benchmarking of grasping and manipulation that can be applied to any dexterous robotic hand

    Application of fractional algorithms in the control of a twin rotor multiple input-multiple output system

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    This paper presents the modelling and control of a laboratory helicopter twin rotor MIMO system using the MatLab package. Firstly, we provide an overview of the system model, secondly, we compare the behaviour of fractional and integer order controllers used a PSO algorithm for the controller optimization in order to obtain the minimum error. Finally, we analyse the system performance and the results obtained with the real helicopter show that fractional algorithms are smoother than conventional PID. Both controllers reveal good output responses but the PID needs more energy to perform the same task.N/

    Caffeine Does Not Augment Markers of Muscle Damage or Leukocytosis Following Resistance Exercise

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    The purpose of this study was to evaluate the effects of caffeine ingestion before a resistance exercise session on markers of muscle damage (CK, LDH, ALT, AST) and leukocyte levels. Methods: Fifteen soccer athletes completed two resistance exercise sessions that differed only in the ingestion of caffeine or a placebo preworkout. Results: CK concentration increased significantly following the caffeine session (415.8 ± 62.8 to 542.0 ± 73.5) and the placebo session (411.5 ± 43.3 to 545.8 ± 59.9), with no significant differences between sessions. Similarly, LDH concentration increased significantly following the caffeine session (377.5 ± 18.0 to 580.5 ± 36.1) and the placebo session (384.8 ± 13.9 to 570.4 ± 36.1), with no significant differences between sessions. Both sessions resulted in significant increases in the total leukocyte count (caffeine = 6.24 ± 2.08 to 8.84 ± 3.41; placebo = 6.36 ± 2.34 to 8.77 ± 3.20), neutrophils (caffeine = 3.37 ± 0.13 to 5.15 ± 0.28; placebo = 3.46 ± 0.17 to 5.12 ± 0.24), lymphocytes (caffeine = 2.19 ± 0.091 to 2.78 ± 0.10; placebo = 2.17 ± 0.100 to 2.75 ± 0.11), and monocytes (caffeine = 0.53 ± 0.02 to 0.72 ± 0.06; placebo = 0.56 ± 0.03 to 0.69 ± 0.04), with no significant differences between sessions. Conclusion: Ingestion of caffeine at 4.5 mg⋅kg−1 did not augment markers of muscle damage or leukocyte levels above that which occurs through resistance exercise alone
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