5,340 research outputs found

    Biomechanics

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    Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

    Vision based motion control for a humanoid head

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    This paper describes the design of a motion control algorithm for a humanoid robotic head, which consists of a neck with four degrees of freedom and two eyes (a stereo pair system) that tilt on a common axis and rotate sideways freely. The kinematic and dynamic properties of the head are analyzed and modeled using screw theory. The motion control algorithm is designed to receive, as an input, the output of a vision processing algorithm and to exploit the redundancy of the system for the realization of the movements. This algorithm is designed to enable the head to focus on and to follow a target, showing human-like motions. The performance of the control algorithm has been tested in a simulated environment and, then, experimentally applied to the real humanoid head

    Exploiting Vestibular Output during Learning Results in Naturally Curved Reaching Trajectories

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    Teaching a humanoid robot to reach for a visual target is a complex problem in part because of the high dimensionality of the control space. In this paper, we demonstrate a biologically plausible simplification of the reaching process that replaces the degrees of freedom in the neck of the robot with sensory readings from a vestibular system. We show that this simplification introduces errors that are easily overcome by a standard learning algorithm. Furthermore, the errors that are necessarily introduced by this simplification result in reaching trajectories that are curved in the same way as human reaching trajectories

    A Developmental Organization for Robot Behavior

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    This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

    Mechatronic design of the Twente humanoid head

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    This paper describes the mechatronic design of the Twente humanoid head, which has been realized in the purpose of having a research platform for human-machine interaction. The design features a fast, four degree of freedom neck, with long range of motion, and a vision system with three degrees of freedom, mimicking the eyes. To achieve fast target tracking, two degrees of freedom in the neck are combined in a differential drive, resulting in a low moving mass and the possibility to use powerful actuators. The performance of the neck has been optimized by minimizing backlash in the mechanisms, and using gravity compensation. The vision system is based on a saliency algorithm that uses the camera images to determine where the humanoid head should look at, i.e. the focus of attention computed according to biological studies. The motion control algorithm receives, as input, the output of the vision algorithm and controls the humanoid head to focus on and follow the target point. The control architecture exploits the redundancy of the system to show human-like motions while looking at a target. The head has a translucent plastic cover, onto which an internal LED system projects the mouth and the eyebrows, realizing human-like facial expressions

    Development of the Vertebral Joints (C3 through T2) in Man

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    Neural Network Modeling of Sensory-Motor Control in Animals

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    National Science Foundation (IRI 90-24877, IRI 87-16960); Air Force Office of Scientific Research (F49620-92-J-0499); Office of Naval Research (N00014-92-J-1309

    Personalized hip joint kinetics during deep squatting in young, athletic adults

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    The goal of this study was to report deep squat hip kinetics in young, athletic adults using a personalized numerical model solution based on inverse dynamics. Thirty-five healthy subjects underwent deep squat motion capture acquisitions and MRI scans of the lower extremities. Musculoskeletal models were personalized using each subject's lower limb anatomy. The average peak hip joint reaction force was 274 percent bodyweight. Average peak hip and knee flexion angles were 107 degrees and 112 degrees respectively. These new findings show that deep squatting kinetics in the younger population differ substantially from the previously reported in vivo data in older subjects

    Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect

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    Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015
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