1
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Affiliation(s)
- Felix Frank
- Volkswagen Machine Learning Research Lab, Munich, Germany
| | | | | | - Botond Cseke
- Volkswagen Machine Learning Research Lab, Munich, Germany
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2
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Jensen GW, van der Smagt P, Luksch H, Straka H, Kohl T. Chronic Multi-Electrode Electromyography in Snakes. Front Behav Neurosci 2022; 15:761891. [PMID: 35069138 PMCID: PMC8777293 DOI: 10.3389/fnbeh.2021.761891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2022] Open
Abstract
Knowledge about body motion kinematics and underlying muscle contraction dynamics usually derives from electromyographic (EMG) recordings. However, acquisition of such signals in snakes is challenging because electrodes either attached to or implanted beneath the skin may unintentionally be removed by force or friction caused from undulatory motion, thus severely impeding chronic EMG recordings. Here, we present a reliable method for stable subdermal implantation of up to eight bipolar electrodes above the target muscles. The mechanical stability of the inserted electrodes and the overnight coverage of the snake body with a “sleeping bag” ensured the recording of reliable and robust chronic EMG activity. The utility of the technique was verified by daily acquisition of high signal-to-noise activity from all target sites over four consecutive days during stimulus-evoked postural reactions in Amazon tree boas and Western diamondback rattlesnakes. The successful demonstration of the chronic recording suggests that this technique can improve acute experiments by enabling the collection of larger data sets from single individuals.
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Affiliation(s)
- Grady W. Jensen
- Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University, Munich, Germany
- ARGMAX.AI Volkswagen Group Machine Learning Research Lab, Munich, Germany
| | - Patrick van der Smagt
- Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University, Munich, Germany
- ARGMAX.AI Volkswagen Group Machine Learning Research Lab, Munich, Germany
- Department of Artificial Intelligence, Faculty of Informatics, Eötvös Lórand University, Budapest, Germany
| | - Harald Luksch
- Chair of Zoology, Technical University of Munich, Freising, Germany
| | - Hans Straka
- Department Biology II, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Tobias Kohl
- Chair of Zoology, Technical University of Munich, Freising, Germany
- *Correspondence: Tobias Kohl
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3
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Dickmann T, Wilhelm NJ, Glowalla C, Haddadin S, van der Smagt P, Burgkart R. An Adaptive Mechatronic Exoskeleton for Force-Controlled Finger Rehabilitation. Front Robot AI 2021; 8:716451. [PMID: 34660703 PMCID: PMC8514640 DOI: 10.3389/frobt.2021.716451] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
This paper presents a novel mechatronic exoskeleton architecture for finger rehabilitation. The system consists of an underactuated kinematic structure that enables the exoskeleton to act as an adaptive finger stimulator. The exoskeleton has sensors for motion detection and control. The proposed architecture offers three main advantages. First, the exoskeleton enables accurate quantification of subject-specific finger dynamics. The configuration of the exoskeleton can be fully reconstructed using measurements from three angular position sensors placed on the kinematic structure. In addition, the actuation force acting on the exoskeleton is recorded. Thus, the range of motion (ROM) and the force and torque trajectories of each finger joint can be determined. Second, the adaptive kinematic structure allows the patient to perform various functional tasks. The force control of the exoskeleton acts like a safeguard and limits the maximum possible joint torques during finger movement. Last, the system is compact, lightweight and does not require extensive peripherals. Due to its safety features, it is easy to use in the home. Applicability was tested in three healthy subjects.
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Affiliation(s)
- Thomas Dickmann
- Orthopaedic Research, Clinic for Orthopaedics and Sport Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nikolas J Wilhelm
- Orthopaedic Research, Clinic for Orthopaedics and Sport Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.,Chair of Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, Germany
| | - Claudio Glowalla
- Orthopaedic Research, Clinic for Orthopaedics and Sport Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sami Haddadin
- Chair of Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, Germany
| | - Patrick van der Smagt
- Machine Learning Research Lab, Volkswagen Group, Munich, Germany.,Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University Munich, Munich, Germany.,Department of Artificial Intelligence, Faculty of Informatics, Eötvös Lórand University, Budapest, Hungary
| | - Rainer Burgkart
- Orthopaedic Research, Clinic for Orthopaedics and Sport Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
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4
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Jensen GW, van der Smagt P, Heiss E, Straka H, Kohl T. SnakeStrike: A Low-Cost Open-Source High-Speed Multi-Camera Motion Capture System. Front Behav Neurosci 2020; 14:116. [PMID: 32848652 PMCID: PMC7416652 DOI: 10.3389/fnbeh.2020.00116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/10/2020] [Indexed: 11/13/2022] Open
Abstract
Current neuroethological experiments require sophisticated technologies to precisely quantify the behavior of animals. In many studies, solutions for video recording and subsequent tracking of animal behavior form a major bottleneck. Three-dimensional (3D) tracking systems have been available for a few years but are usually very expensive and rarely include very high-speed cameras; access to these systems for research is limited. Additionally, establishing custom-built software is often time consuming – especially for researchers without high-performance programming and computer vision expertise. Here, we present an open-source software framework that allows researchers to utilize low-cost high-speed cameras in their research for a fraction of the cost of commercial systems. This software handles the recording of synchronized high-speed video from multiple cameras, the offline 3D reconstruction of that video, and a viewer for the triangulated data, all functions previously also available as separate applications. It supports researchers with a performance-optimized suite of functions that encompass the entirety of data collection and decreases processing time for high-speed 3D position tracking on a variety of animals, including snakes. Motion capture in snakes can be particularly demanding since a strike can be as short as 50 ms, literally twice as fast as the blink of an eye. This is too fast for faithful recording by most commercial tracking systems and therefore represents a challenging test to our software for quantification of animal behavior. Therefore, we conducted a case study investigating snake strike speed to showcase the use and integration of the software in an existing experimental setup.
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Affiliation(s)
- Grady W Jensen
- Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University Munich, Munich, Germany.,argmax.ai, Volkswagen Group Machine Learning Research Lab, Munich, Germany
| | - Patrick van der Smagt
- Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University Munich, Munich, Germany.,argmax.ai, Volkswagen Group Machine Learning Research Lab, Munich, Germany.,Department of Artificial Intelligence, Faculty of Informatics, Eötvös Lórand University, Budapest, Hungary
| | - Egon Heiss
- Institute of Zoology and Evolutionary Research, Friedrich-Schiller-University of Jena, Jena, Germany
| | - Hans Straka
- Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University Munich, Munich, Germany.,Department Biology II, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Tobias Kohl
- Chair of Zoology, Technical University of Munich, Freising, Germany
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5
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Staffler B, Berning M, Boergens KM, Gour A, van der Smagt P, Helmstaedter M. SynEM, automated synapse detection for connectomics. eLife 2017; 6:e26414. [PMID: 28708060 PMCID: PMC5658066 DOI: 10.7554/elife.26414] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 07/12/2017] [Indexed: 11/13/2022] Open
Abstract
Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.
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Affiliation(s)
- Benedikt Staffler
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Manuel Berning
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Kevin M Boergens
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Anjali Gour
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | | | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
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Höppner H, Große-Dunker M, Stillfried G, Bayer J, van der Smagt P. Key Insights into Hand Biomechanics: Human Grip Stiffness Can Be Decoupled from Force by Cocontraction and Predicted from Electromyography. Front Neurorobot 2017; 11:17. [PMID: 28588472 PMCID: PMC5438998 DOI: 10.3389/fnbot.2017.00017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Accepted: 03/09/2017] [Indexed: 11/13/2022] Open
Abstract
We investigate the relation between grip force and grip stiffness for the human hand with and without voluntary cocontraction. Apart from gaining biomechanical insight, this issue is particularly relevant for variable-stiffness robotic systems, which can independently control the two parameters, but for which no clear methods exist to design or efficiently exploit them. Subjects were asked in one task to produce different levels of force, and stiffness was measured. As expected, this task reveals a linear coupling between force and stiffness. In a second task, subjects were then asked to additionally decouple stiffness from force at these force levels by using cocontraction. We measured the electromyogram from relevant groups of muscles and analyzed the possibility to predict stiffness and force. Optical tracking was used for avoiding wrist movements. We found that subjects were able to decouple grip stiffness from force when using cocontraction on average by about 20% of the maximum measured stiffness over all force levels, while this ability increased with the applied force. This result contradicts the force-stiffness behavior of most variable-stiffness actuators. Moreover, we found the thumb to be on average twice as stiff as the index finger and discovered that intrinsic hand muscles predominate our prediction of stiffness, but not of force. EMG activity and grip force allowed to explain 72 ± 12% of the measured variance in stiffness by simple linear regression, while only 33 ± 18% variance in force. Conclusively the high signal-to-noise ratio and the high correlation to stiffness of these muscles allow for a robust and reliable regression of stiffness, which can be used to continuously teleoperate compliance of modern robotic hands.
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Affiliation(s)
- Hannes Höppner
- Bionics Lab, Institute of Robotics and Mechatronics, German Aerospace Center DLR e.V., Oberpfaffenhofen, Wessling, Germany
| | - Maximilian Große-Dunker
- Bionics Lab, Institute of Robotics and Mechatronics, German Aerospace Center DLR e.V., Oberpfaffenhofen, Wessling, Germany
| | - Georg Stillfried
- Bionics Lab, Institute of Robotics and Mechatronics, German Aerospace Center DLR e.V., Oberpfaffenhofen, Wessling, Germany
| | - Justin Bayer
- Department of Informatics, Technische Universität München, Munich, Germany
| | - Patrick van der Smagt
- Department of Informatics, Technische Universität München, Munich, Germany.,fortiss, TUM affiliated Institute, Munich, Germany
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7
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Falotico E, Vannucci L, Ambrosano A, Albanese U, Ulbrich S, Vasquez Tieck JC, Hinkel G, Kaiser J, Peric I, Denninger O, Cauli N, Kirtay M, Roennau A, Klinker G, Von Arnim A, Guyot L, Peppicelli D, Martínez-Cañada P, Ros E, Maier P, Weber S, Huber M, Plecher D, Röhrbein F, Deser S, Roitberg A, van der Smagt P, Dillman R, Levi P, Laschi C, Knoll AC, Gewaltig MO. Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. Front Neurorobot 2017; 11:2. [PMID: 28179882 PMCID: PMC5263131 DOI: 10.3389/fnbot.2017.00002] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 01/04/2017] [Indexed: 11/13/2022] Open
Abstract
Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain-body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 "Neurorobotics" of the Human Brain Project (HBP). At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.
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Affiliation(s)
- Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | | | - Ugo Albanese
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Stefan Ulbrich
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Juan Camilo Vasquez Tieck
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Georg Hinkel
- Department of Software Engineering (SE), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Jacques Kaiser
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Igor Peric
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Oliver Denninger
- Department of Software Engineering (SE), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Nino Cauli
- Computer and Robot Vision Laboratory, Instituto de Sistemas e Robotica, Instituto Superior Tecnico, Lisbon, Portugal
| | - Murat Kirtay
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Arne Roennau
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Gudrun Klinker
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Luc Guyot
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
| | - Daniel Peppicelli
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
| | - Pablo Martínez-Cañada
- Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain
| | - Patrick Maier
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Sandro Weber
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Manuel Huber
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - David Plecher
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Florian Röhrbein
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Stefan Deser
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Alina Roitberg
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Rüdiger Dillman
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Paul Levi
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Alois C. Knoll
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
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Abstract
This short communication presents preliminary results from an extensive investigation of joint modelling for the human hand. We use finger and hand movement data recorded from both hands of 110 subjects using passive reflective markers on the skin. Furthermore, we use data which was recorded from a single Thiel-fixated cadaver hand using also passive reflective markers but fixed to the bone. Our data clearly demonstrate that, for wrist and finger joints, hinge joint models are sufficiently accurate to describe their movement in Cartesian space.
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Affiliation(s)
- Agneta Gustus
- Klinikum rechts der Isar der TU München, Ismaninger Straße 22, 81675 München, Germany; www.brml.org.
| | - Patrick van der Smagt
- Technische Universität München, Arcisstraße 21, 80333 München, Germany; fortiss, Guerickestraße 25, 80805 München, Germany; www.brml.org
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Urbanek H, van der Smagt P. iEMG: Imaging electromyography. J Electromyogr Kinesiol 2016; 27:1-9. [DOI: 10.1016/j.jelekin.2016.01.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 11/15/2015] [Accepted: 01/18/2016] [Indexed: 11/30/2022] Open
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Höppner H, McIntyre J, van der Smagt P. Task dependency of grip stiffness--a study of human grip force and grip stiffness dependency during two different tasks with same grip forces. PLoS One 2013; 8:e80889. [PMID: 24324643 PMCID: PMC3852021 DOI: 10.1371/journal.pone.0080889] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Accepted: 10/07/2013] [Indexed: 11/19/2022] Open
Abstract
It is widely known that the pinch-grip forces of the human hand are linearly related to the weight of the grasped object. Less is known about the relationship between grip force and grip stiffness. We set out to determine variations to these dependencies in different tasks with and without visual feedback. In two different settings, subjects were asked to (a) grasp and hold a stiffness-measuring manipulandum with a predefined grip force, differing from experiment to experiment, or (b) grasp and hold this manipulandum of which we varied the weight between trials in a more natural task. Both situations led to grip forces in comparable ranges. As the measured grip stiffness is the result of muscle and tendon properties, and since muscle/tendon stiffness increases more-or-less linearly as a function of muscle force, we found, as might be predicted, a linear relationship between grip force and grip stiffness. However, the measured stiffness ranges and the increase of stiffness with grip force varied significantly between the two tasks. Furthermore, we found a strong correlation between regression slope and mean stiffness for the force task which we ascribe to a force stiffness curve going through the origin. Based on a biomechanical model, we attributed the difference between both tasks to changes in wrist configuration, rather than to changes in cocontraction. In a new set of experiments where we prevent the wrist from moving by fixing it and resting it on a pedestal, we found subjects exhibiting similar stiffness/force characteristics in both tasks.
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Affiliation(s)
- Hannes Höppner
- Institute of Robotics and Mechatronics, German Aerospace Center, Wessling, Germany
| | - Joseph McIntyre
- Centre d'Etudes de la Sensorimotricité, Centre National de la Recherche Scientifique and Université Paris Descartes, Paris, France
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Braun DJ, Petit F, Huber F, Haddadin S, van der Smagt P, Albu-Schaffer A, Vijayakumar S. Robots Driven by Compliant Actuators: Optimal Control Under Actuation Constraints. IEEE T ROBOT 2013. [DOI: 10.1109/tro.2013.2271099] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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13
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Abstract
Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.
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Affiliation(s)
- Claudio Castellini
- DLR / German Aerospace Center, Institute of Robotics and Mechatronics, Muenchnerstr. 20, 82234, Wessling, Germany.
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14
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Abstract
In order to stably grasp an object with an artificial hand, a priori knowledge of the object's properties is a major advantage, especially to ensure subsequent manipulation of the object held by the hand. This is also true for hand prostheses: pre-shaping of the hand while approaching the object, similar to able-bodied, allows the wearer for a much faster and more intuitive way of handling and grasping an object. For hand prostheses, it would be advantageous to obtain this information about object properties from a surface electromyography (sEMG) signal, which is already present and used to control the active prosthetic hand. We describe experiments in which human subjects grasp different objects at different positions while their muscular activity is recorded through eight sEMG electrodes placed on the forearm. Results show that sEMG data, gathered before the hand is in contact with the object, can be used to obtain relevant information on object properties such as size and weight.
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Affiliation(s)
- Nadine Fligge
- German Aerospace Center (DLR), Center for Robotics and Mechatronics, Muenchner Strasse 20, D-82234 Oberpfaffenhofen-Wessling, Germany.
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Gustus A, Stillfried G, Visser J, Jörntell H, van der Smagt P. Human hand modelling: kinematics, dynamics, applications. Biol Cybern 2012; 106:741-755. [PMID: 23132432 DOI: 10.1007/s00422-012-0532-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Accepted: 10/15/2012] [Indexed: 06/01/2023]
Abstract
An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.
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16
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Affiliation(s)
- Markus Grebenstein
- a Institute of Robotics and Mechatronics, German Aerospace Center/DLR Oberpfaffenhofen, PO Box 1116, 82230 Wessling, Germany
| | - Patrick van der Smagt
- b Institute of Robotics and Mechatronics, German Aerospace Center/DLR Oberpfaffenhofen, PO Box 1116, 82230 Wessling, Germany;,
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17
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Rückstieß T, Osendorfer C, van der Smagt P. Sequential Feature Selection for Classification. AI 2011: Advances in Artificial Intelligence 2011. [DOI: 10.1007/978-3-642-25832-9_14] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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18
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Abstract
The construction of robotic systems that can move the way humans do, with respect to agility, stability and precision, is a necessary prerequisite for the successful integration of robotic systems in human environments. We explain human-centered views on robotics, based on the three basic ingredients (1) actuation; (2) sensing; and (3) control, and formulate detailed examples thereof.
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Abstract
One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible.
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Affiliation(s)
- Claudio Castellini
- LIRA-Lab, University of Genova, viale F. Causa, 13, 16145, Genova, Italy.
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van der Smagt P, Bullock D. APPL INTELL 2002; 17:7-10. [DOI: 10.1023/a:1015762314222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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