71,053 research outputs found

    Animating Human Muscle Structure

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    Graphical simulations of human muscle motion and deformation are of great interest to medical education. In this article, the authors present a technique for simulating muscle deformations by combining physically and geometrically based computations to reduce computation cost and produce fast, accurate simulations

    Framework for Dynamic Evaluation of Muscle Fatigue in Manual Handling Work

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    Muscle fatigue is defined as the point at which the muscle is no longer able to sustain the required force or work output level. The overexertion of muscle force and muscle fatigue can induce acute pain and chronic pain in human body. When muscle fatigue is accumulated, the functional disability can be resulted as musculoskeletal disorders (MSD). There are several posture exposure analysis methods useful for rating the MSD risks, but they are mainly based on static postures. Even in some fatigue evaluation methods, muscle fatigue evaluation is only available for static postures, but not suitable for dynamic working process. Meanwhile, some existing muscle fatigue models based on physiological models cannot be easily used in industrial ergonomic evaluations. The external dynamic load is definitely the most important factor resulting muscle fatigue, thus we propose a new fatigue model under a framework for evaluating fatigue in dynamic working processes. Under this framework, virtual reality system is taken to generate virtual working environment, which can be interacted with the work with haptic interfaces and optical motion capture system. The motion information and load information are collected and further processed to evaluate the overall work load of the worker based on dynamic muscle fatigue models and other work evaluation criterions and to give new information to characterize the penibility of the task in design process.Comment: International Conference On Industrial Technology, Chengdu : Chine (2008

    Wearable Capacitive-based Wrist-worn Gesture Sensing System

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    Gesture control plays an increasingly significant role in modern human-machine interactions. This paper presents an innovative method of gesture recognition using flexible capacitive pressure sensor attached on user’s wrist towards computer vision and connecting senses on fingers. The method is based on the pressure variations around the wrist when the gesture changes. Flexible and ultrathin capacitive pressure sensors are deployed to capture the pressure variations. The embedding of sensors on a flexible substrate and obtain the relevant capacitance require a reliable approach based on a microcontroller to measure a small change of capacitive sensor. This paper is addressing these challenges, collect and process the measured capacitance values through a developed programming on LabVIEW to reconstruct the gesture on computer. Compared to the conventional approaches, the wrist-worn sensing method offerings a low-cost, lightweight and wearable prototype on the user’s body. The experimental result shows that the potentiality and benefits of this approach and confirms that accuracy and number of recognizable gestures can be improved by increasing number of sensor

    Exploring the Affective Loop

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    Research in psychology and neurology shows that both body and mind are involved when experiencing emotions (Damasio 1994, Davidson et al. 2003). People are also very physical when they try to communicate their emotions. Somewhere in between beings consciously and unconsciously aware of it ourselves, we produce both verbal and physical signs to make other people understand how we feel. Simultaneously, this production of signs involves us in a stronger personal experience of the emotions we express. Emotions are also communicated in the digital world, but there is little focus on users' personal as well as physical experience of emotions in the available digital media. In order to explore whether and how we can expand existing media, we have designed, implemented and evaluated /eMoto/, a mobile service for sending affective messages to others. With eMoto, we explicitly aim to address both cognitive and physical experiences of human emotions. Through combining affective gestures for input with affective expressions that make use of colors, shapes and animations for the background of messages, the interaction "pulls" the user into an /affective loop/. In this thesis we define what we mean by affective loop and present a user-centered design approach expressed through four design principles inspired by previous work within Human Computer Interaction (HCI) but adjusted to our purposes; /embodiment/ (Dourish 2001) as a means to address how people communicate emotions in real life, /flow/ (Csikszentmihalyi 1990) to reach a state of involvement that goes further than the current context, /ambiguity/ of the designed expressions (Gaver et al. 2003) to allow for open-ended interpretation by the end-users instead of simplistic, one-emotion one-expression pairs and /natural but designed expressions/ to address people's natural couplings between cognitively and physically experienced emotions. We also present results from an end-user study of eMoto that indicates that subjects got both physically and emotionally involved in the interaction and that the designed "openness" and ambiguity of the expressions, was appreciated and understood by our subjects. Through the user study, we identified four potential design problems that have to be tackled in order to achieve an affective loop effect; the extent to which users' /feel in control/ of the interaction, /harmony and coherence/ between cognitive and physical expressions/,/ /timing/ of expressions and feedback in a communicational setting, and effects of users' /personality/ on their emotional expressions and experiences of the interaction

    Freeform User Interfaces for Graphical Computing

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    報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

    Probabilistic movement modeling for intention inference in human-robot interaction.

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    Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
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