Prof. Dr. rer. nat. Ioannis Iossifidis

AUSGEWÄHLTE PUBLIKATIONEN

• anthropomorphic robot arm arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems force sensing grippers haptic interface human anatomy human operators intuition human perception inverse kinematics Machine Learning man machine interaction man-machine systems man-machine-interaction manipulators movement model object recognition operators gaze operators gesture robot manipulator control robot vision robotic assistant robots gripper sensory channels

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  21 Einträge « ‹ 1 von 3 › »

  2023

  21.

  Sziburis, Tim; Blex, Susanne; Iossifidis, Ioannis

  Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders Proceedings Article

  In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.

  Abstract | BibTeX | Schlagwörter: movement model

  @inproceedings{sziburisVariabilityStudyHuman2023,
  
  title = {Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders},
  
  author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},
  
  year  = {2023},
  
  date = {2023-09-15},
  
  urldate = {2023-09-15},
  
  booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
  
  publisher = {BCCN Bernstein Network Computational Network},
  
  abstract = {Variability analysis bears the potential to differentiate between healthy and pathological human movements [1]. Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated. In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used. The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects. Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths [2]. For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping. In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.},
  
  keywords = {movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Variability analysis bears the potential to differentiate between healthy and pathological human movements [1]. Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated. In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used. The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects. Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths [2]. For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping. In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.

  Schließen

  2022

  20.

  Schmidt, Marie Dominique; Glasmachers, Tobias; Iossifidis, Ioannis

  From Motion to Muscle Artikel

  In: arXiv: 2201.11501 [cs.LG], 2022.

  Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network

  @article{schmidt2022motion,
  
  title = {From Motion to Muscle},
  
  author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {https://doi.org/10.48550/arXiv.2201.11501},
  
  year  = {2022},
  
  date = {2022-01-01},
  
  urldate = {2022-01-01},
  
  journal = {arXiv: 2201.11501 [cs.LG]},
  
  keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Schließen

  • doi:https://doi.org/10.48550/arXiv.2201.11501

  Schließen

  19.

  Schmidt, Marie Dominique; Glasmachers, Tobias; Iossifidis, Ioannis

  Motion Intention Prediction Proceedings Article

  In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.

  Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network

  @inproceedings{schmidtMotionIntentionPrediction2022a,
  
  title = {Motion Intention Prediction},
  
  author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
  
  year  = {2022},
  
  date = {2022-01-01},
  
  urldate = {2022-01-01},
  
  booktitle = {FENS, Forum 2022},
  
  publisher = {FENS, Federation of European Neuroscience Societies},
  
  abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},
  
  keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.

  Schließen

  2021

  18.

  Schmidt, Marie Dominique; Glasmachers, Tobias; Iossifidis, Ioannis

  Artificially Generated Muscle Signals Proceedings Article

  In: Bernstein Conference, 2021.

  Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network

  @inproceedings{schmidtArtificiallyGeneratedMuscle2021b,
  
  title = {Artificially Generated Muscle Signals},
  
  author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p111},
  
  year  = {2021},
  
  date = {2021-10-01},
  
  urldate = {2021-10-01},
  
  publisher = {Bernstein Conference},
  
  keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p111

  Schließen

  17.

  Sziburis, Tim; Blex, Susanne; Glasmachers, Tobias; Rano, Inaki; Iossifidis, Ioannis

  Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article

  In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.

  Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model

  @inproceedings{sziburisModellingGenerationHuman2021,
  
  title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},
  
  author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},
  
  doi = {10.12751/nncn.bc2021.p078},
  
  year  = {2021},
  
  date = {2021-09-15},
  
  urldate = {2021-09-15},
  
  booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
  
  publisher = {BCCN Bernstein Network Computational Network},
  
  keywords = {BCI, Machine Learning, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  • doi:10.12751/nncn.bc2021.p078

  Schließen

  2018

  16.

  Hussain, Muhammad Ayaz; Klaes, Christian; Iossifidis, Ioannis

  Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Proceedings Article

  In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model

  @inproceedings{bccn18,
  
  title = {Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title},
  
  author = {Muhammad Ayaz Hussain and Christian Klaes and Ioannis Iossifidis},
  
  year  = {2018},
  
  date = {2018-01-01},
  
  urldate = {2018-01-01},
  
  booktitle = {BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018},
  
  publisher = {BCCN},
  
  abstract = {To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror

  Schließen

  2017

  15.

  Iossifidis, Ioannis; Hussain, Muhammad Ayaz; Klaes, Christian

  Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017a,
  
  title = {Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Muhammad Ayaz Hussain and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.

  Schließen

  14.

  Iossifidis, Ioannis; Klaes, Christian

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017b,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.

  Schließen

  13.

  Klaes, Christian; Iossifidis, Ioannis

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Konferenz

  SfN Meeting 2017, 2017.

  BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @conference{nokey,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Christian Klaes and Ioannis Iossifidis},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  booktitle = {SfN Meeting 2017},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {conference}
  
  }
  
  

  Schließen

  2013

  12.

  Rano, Inaki; Iossifidis, Ioannis

  Modelling human arm motion through the attractor dynamics approach Proceedings Article

  In: 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013, S. 2088–2093, 2013, ISBN: 9781479927449.

  Abstract | Links | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, Dynamical systems, movement model

  @inproceedings{Rano2013,
  
  title = {Modelling human arm motion through the attractor dynamics approach},
  
  author = {Inaki Rano and Ioannis Iossifidis},
  
  doi = {10.1109/ROBIO.2013.6739777},
  
  isbn = {9781479927449},
  
  year  = {2013},
  
  date = {2013-01-01},
  
  booktitle = {2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013},
  
  pages = {2088--2093},
  
  abstract = {Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.},
  
  keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  Movement generation in robotics is an old problem with many excellent solutions. Most of them, however, look for optimality according to some metrics, but have no biological inspiration or cannot be used to imitate biological motion. For a human these techniques behave in a non-naturalistic way. This poses a problem for instance in human-robot interaction and, in general, for a good acceptance of robots in society. The present work presents a new analysis of the attractor dynamics approach to movement generation used in an anthropomorphic robot arm. Our analysis points to the possibility of using this approach to generate human-like arm trajectories in robots. One key property of human trajectories in pick-and-place tasks is the planarity of the trajectory of the end effector in 3D space. We show that this feature is also displayed by the attractor dynamic approach and, therefore, is a good candidate to the generation of naturalistic arm movements. textcopyright 2013 IEEE.

  Schließen

  • doi:10.1109/ROBIO.2013.6739777

  Schließen

  21 Einträge « ‹ 1 von 3 › »