Veröffentlichungen

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Iossifidis2013a,

title = {Modeling Human Arm Motion by Means of Attractor Dynamics Approach},

author = {Ioannis Iossifidis and Ianki Rano},

year  = {2013},

date = {2013-01-01},

booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2013)},

abstract = {Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. 

 

 In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.},

keywords = {arm movement model, Autonomous robotics, Dynamical systems, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Reimannd,

title = {End-effector obstacle avoidance using multiple dynamic variables},

author = {Hendrik Reimann and Ioannis Iossifidis and Gregor Schöner},

year  = {2010},

date = {2010-01-01},

booktitle = {ISR / ROBOTIK 2010},

address = {Munich, Germany},

abstract = {The avoidance of obstacles is a crucial part of the generation of behavior for autonomos robotic agents. A standard method to produce trajectories to a given target that avoids a number of possibly mobile obstacles is the potential field approach introduced by Khatib, where an artificial potential field is constructed around target and obstacles, with the target acting as a global minimum and the obstacles as local maxima, the gradient of which is used to determine the (artificial) force acting on the robot at any moment. While the potential field approach has been used extensively for vehicle motion in a plane, applications for robotic manipulators suffer from a high level of complexity due to the formulation of constraints as forces necessitating the inclusion of dynamic properties of the manipulator into the system. We pursue a different solution to the problem of manipulator obstacle avoidance based on the dynamic approach to robotics, which states that all behavioral constraints for the generation of movement should be formulated as attractors or repellors of a dynamical systems. The problem of behavior design is thus separated from the control problem of how to realize the designed behavior, bringing the advantage of simplicity in the formulation of the former.},

keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model, obstacle avoidance},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Grimm2010b,

title = {Behavioral Organization for Mobile Robotic Systems: An Attractor Dynamics Approach},

author = {Matthias Grimm and Ioannis Iossifidis},

year  = {2010},

date = {2010-01-01},

booktitle = {ISR / ROBOTIK 2010},

address = {Munich, Germany},

abstract = {Autonomous systems generate different behaviors based on the perceived environmental situation. The organization of a set of behaviors plays an important role in the field of autonomous robotics. The organization architecture must be flexible, so that behavioral changes are possible if the sensory information changes. Furthermore, behavioral organization must be stable, so that small changes in sensory information do not lead to oscillations. To achieve this, all behaviors, but also the underlying organization architecture, are based on continuous dynamical systems. They are characterized by a set of dynamical variables, also referred to as state variables. These variables represent the activation or deactivation of a particular behavior. Elementary behaviors are dependent on the sensor input in a way, that changes of the sensorial information lead to qualitatively different behaviors. The so-called sensor context denotes whether a behavior is applicable in the current sensor situation or not. However, for complex systems consisting of many elementary behaviors, it is necessary to take logical conditions into account to generate a sequence of behaviors. Furthermore, some elementary behaviors can or even must run in parallel, while others exclude each other. This internal information requires knowledge about the logical interaction of the behaviors and is stored within binary matrices. This makes the overall organization structure very flexible and easy to extend. We present the architecture using the example of approaching and passing a door. The robot has to navigate from one room to another while simultaneously avoiding obstacles in its pathway.},

keywords = {Autonomous robotics, behavior generation, Dynamical systems, movement model, movile robot},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sandamirskayasubmitted,

title = {Natural human-robot interaction through spatial language: a dynamic neural fields approach},

author = {Yulia Sandamirskaya and John Lipinski and Ioannis Iossifidis and G Schöner},

url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5598671},

issn = {1944-9445},

year  = {2010},

date = {2010-01-01},

booktitle = {Proc. 19th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2010)},

pages = {600--607},

publisher = {IEEE},

keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, man machine interaction, movement model, speech recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Iossifidis2004a,

title = {Attractor dynamics approach for autonomous collision-free path generation in 3d-space for an 7 dof robot arm},

author = {Ioannis Iossifidis and Gregor Schöner},

year  = {2004},

date = {2004-01-01},

booktitle = {Proceedings of the ROBOTIK 2004, Leistungsstand - Anwendungen - Visionen - Trends, number 1841 in VDI-Berichte},

pages = {815--822},

publisher = {VDI Verlag},

address = {München, Germany},

organization = {VDI/VDE},

keywords = {arm movement model, Autonomous robotics, collision avoidance, Dynamical systems, inverse kinematics, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}