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8th International Conference on Computer Vision Theory and Applications, VISAPP 2013

作者： Lanz, Cornelia Olgay, Birant Sibel Denzler, Joachim Gross, Horst-Michael Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Computer Vision Group Friedrich Schiller University Jena Jena Germany

ISBN: (纸本)9789898565471

In this work, we propose an approach for the unexplored topic of therapeutic facial exercise recognition using depth images. In cooperation with speech therapists, we determined nine exercises that are beneficial for therapy of patients suffering from dysfunction of facial movements. Our approach employs 2.5D images and 3D point clouds, which were recorded using Microsoft's Kinect. Extracted features comprise the curvature of the face surface and characteristic profiles that are derived using distinctive landmarks. We evaluate the discriminative power and the robustness of the features with respect to the above-mentioned application scenario. Using manually located face regions for feature extraction, we achieve an average recognition accuracy of about 91% for the nine facial exercises. However in a real-world scenario manual localization of regions for feature extraction is not feasible. Therefore, we additionally examine the robustness of the features and show, that they are beneficial for a real-world, fully automated scenario as well.

关键词： Feature extraction

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Using surface electromyography to predict single finger forces

Using surface electromyography to predict single finger forc...

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IEEE RAS & EMBS International Conference on Biomedical robotics and Biomechatronics

作者： Claudio Castellini Risto Koiva Institute of Robotics and Mechatronics DLR (German Aerospace Center) Neuroinformatics Group Center of Excellence Cognitive Interaction Technology Bielefeld University

ISBN: (纸本)9781457711992

Surface electromyography (sEMG) of the forearm is an active research topic since the 1990s in the rehabilitation robotics/machine learning community, as it can be used to predict the hand posture and overall grip force. We hereby advance the state of the art by describing a multi-subject experiment in which sEMG is successfully used to predict simultaneous forces applied by a human subject at the fingertips, that is, when six voluntary muscle contractions (VMCs) are elicited (flexion of the little, ring, middle and index fingers, thumb rotation and thumb adduction). Using a multi-sensor setup sEMG activity of the forearm of a human subject and the forces exerted at the fingertips are measured;a Support Vector Machine is then used to associate sEMG signals and forces. Our results clearly show that sEMG can be used to predict the required forces with an error as small as 1.5% of the sensor range. Targeted positioning of the electrodes is not required. The prediction is uniformly accurate across all VMCs and all 12 subjects considered, and it is robust against subsampling. This result goes in the direction of enabling natural force/impedance control of a highly dexterous prosthetic hand over a continuous, infinite manifold of force configurations, rather than using posture classification like in the traditional approach.

关键词： SEMA5B gene human subject Surface Electromyography Force Forearm state of the art Support Vector Network Fingers

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FFLS: An accurate linear device for measuring synergistic finger contractions

FFLS: An accurate linear device for measuring synergistic fi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Risto Kõiva Barbara Hilsenbeck Claudio Castellini Neuroinformatics Group Center of Excellence Cognitive Interaction Technology Bielefeld University Bielefeld Germany German AeroSpace Center (DLR) Institute of Robotics and Mechatronics Wessling Germany

After decades of theoretical study in physiology and neurology communities, the paradigm of muscle synergies is now being explored in rehabilitation robotics as a strategy to control mechanical artifacts with many degrees-of-freedom (DoF) in a simple yet effective and human-like way. In particular, muscle synergies during grasping and in graded-force tasks are of great interest for the control of dexterous hand prostheses. To this end, we have designed and tested a novel device to accurately and simultaneously measure fingertip forces. The device, called FFLS (Finger-Force Linear Sensor), measures the forces applied by the human fingertips in both directions (flexion and extension of index, middle, ring and little finger plus thumb rotation and abduction/adduction). It is suited for several different hand sizes, enforces high accuracy in the measurement and its signal is guaranteed to be linear in a high range of forces (100N in both directions for each finger). It outputs six analog voltages (±10V), suited for processing with a DAQ card.

关键词： Thumb Force measurement Robot sensing systems Force Strain Muscles Indexes

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Experimental Evaluation of Human Grasps Using a Sensorized Object

Experimental Evaluation of Human Grasps Using a Sensorized O...

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IEEE RAS & EMBS International Conference on Biomedical robotics and Biomechatronics

作者： Maximo A. Roa Risto Koiva Claudio Castellini Institute of Robotics and Mechatronics DLR (German Aerospace Center) Neuroinformatics Group Center of Excellence Cognitive Interaction Technology Bielefeld University

ISBN: (纸本)9781457711992

Grasp quality measures have been studied for long time, given their importance to evaluate the goodness/convenience of a grasp made with a robotic hand. However, the application of these quality measures to grasps made by humans has just recently received some attention. This paper presents an experimental evaluation and comparison of different measures, using data obtained with a sensorized object. The experiment compares power grasps and precision grasps obtained with different number of fingers. The results intend to be a guide to the application of such qualities in the evaluation of robotic grasp actions.

关键词： Grasp Human

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

作者： Hans, Alexander Duell, Siegmund Udluft, Steffen Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Corporate Technology Munich Munich Germany

ISBN: (纸本)9781424498888

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications. © 2011 IEEE.

关键词： Reinforcement learning

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, (ADPRL)

作者： Alexander Hans Siegmund Duell Steffen Udluft Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Munich Germany

关键词： Uncertainty Equations Markov processes Benchmark testing Approximation algorithms Machine learning Histograms

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A monocular pointing pose estimator for gestural instruction of a mobile robot

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International Journal of Advanced Robotic Systems 2007年第1 SPEC. ISS.期4卷 139-150页

作者： Richarz, Jan Scheidig, Andrea Martin, Christian Müller, Steffen Gross, Horst-Michael Intelligent Systems Group Robotics Research Institute University of Dortmund Germany Department of Neuroinformatics and Cognitive Robotics Ilmenau Technical University Germany

We present an important aspect of our human-robot communication interface which is being developed in the context of our long-term research framework PERSES dealing with highly interactive mobile companion robots. Based on a multi-modal people detection and tracking system, we present a hierarchical neural architecture that estimates a target point at the floor indicated by a pointing pose, thus enabling a user to navigate a mobile robot to a specific target position in his local surroundings by means of pointing. In this context, we were especially interested in determining whether it is possible to accomplish such a target point estimator using only monocular images of low-cost cameras. The estimator has been implemented and experimentally investigated on our mobile robotic assistant HOROS. Although only monocular image data of relatively poor quality were utilized, the estimator accomplishes a good estimation performance, achieving an accuracy better than that of a human viewer on the same data. The achieved recognition results demonstrate that it is in fact possible to realize a user-independent pointing direction estimation using monocular images only, but further efforts are necessary to improve the robustness of this approach for everyday application.

关键词： Gesture recognition

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Tracking of Human Body Parts using the Multiocular Contracting Curve Density Algorithm

Tracking of Human Body Parts using the Multiocular Contracti...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Markus Hahn Lars Kruger Christian Wohler Horst-Michael Gross Environment Perception DaimlerChrysler AG Group Research Ulm Germany Department of Neuroinformatics and Cognitive Robotics Technical University Ilmenau Ilmenau Germany

In this contribution we introduce the multiocular contracting curve density algorithm (MOCCD), a novel method for fitting a 3D parametric curve. The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered background is examined. The developed system can be applied to a variety of body parts, as the object model is replaceable in a simple manner. Based on the example of tracking the human hand-forearm limb it is shown that the use of three MOCCD algorithms with three different kinematic models within the system leads to an accurate and temporally stable tracking. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. The investigations are performed on 14 real-world test sequences. These contain movements of different hand-forearm configurations in front of a complex cluttered background. We find that the use of three cameras is essential for an accurate and temporally stable system performance since otherwise the pose estimation and tracking results are strongly affected by the aperture problem. Our best method achieves 95% recognition rate, compared to about 30% for the reference methods of 3D active contours and a curve model tracked by a particle filter. Hence only 5% of the estimated model points exceed a distance of 12 cm with respect to the ground truth, using the proposed method.

关键词： Humans Curve fitting Biological system modeling Kinematics Performance evaluation Testing Cameras System performance Apertures Active contours

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There you go! - Estimating pointing gestures in monocular images for mobile robot instruction

There you go! - Estimating pointing gestures in monocular im...

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RO-MAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication

作者： Richarz, J. Martin, C. Scheidig, A. Gross, H.M. Intelligent Systems Group Robotics Research Institute University of Dortmund Germany Department of Neuroinformatics and Cognitive Robotics Ilmenau Technical University Germany

ISBN: (纸本)1424405653

In this paper, we present a neural architecture that is capable of estimating a target point from a pointing gesture, thus enabling a user to command a mobile robot to a specific position in his local surroundings by means of pointing. In this context, we were especially interested to determine whether it is possible to implement a target point estimator using only monocular images of low-cost webcams. The feature extraction is also quite straightforward: We use a gabor jet to extract the feature vector from the normalized camera images;and a cascade of Multi Layer Perceptron (MLP) Classifiers as estimator. The System was implemented and tested on our mobile robotic assistant HOROS. The results indicate that it is in fact possible to realize a pointing estimator using monocular image data, but further efforts are necessary to improve the accuracy and robustness of our approach. © 2006 IEEE.

关键词： Mobile robots

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Hey, I'm over here - How can a robot attract people's attention?

Hey, I'm over here - How can a robot attract people's attent...

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14th IEEE Workshop on Robot and Human Interactive Communication, RO-MAN 2005

作者： Finke, Markus Koay, Kheng Lee Dautenhahn, Kerstin Nehaniv, Chrystopher L. Walters, Michael L. Saunders, Joe Neuroinformatics and Cognitive Robotics Group Faculty of Informatics and Automatization Technical University Ilmenau P.O.Box 100565 98684 Ilmenau Germany Adaptive Systems Research Group School of Computer Science University of Hertfordshire College Lane Hatfield Herts AL10 9AB United Kingdom

ISBN: (纸本)0780392752

This paper describes how sonar sensors can be used to recognize human movements. The robot distinguishes objects from humans by assuming that only people move by themselves. Two methods using either rules or Hidden Markov Models are described. The robot classifies different movements to provide a basis for judging if a person is interested in an interaction. A comparison of two experiment results is presented. The use of orienting cues by the robot in response to detected human movement for eliciting interaction is also studied. © 2005 IEEE.

关键词： Robots

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