Fast fictitious surface charge method for calculation of torso surface potentials. - In: 2023 24th International Conference on the Computation of Electromagnetic Fields (COMPUMAG), (2023), insges. 4 S.
Well-established forward modeling methods in electrocardiography (ECG) require fine meshes to calculate the electric scalar potential at the body surface with high accuracy. We introduce a fast fictitious surface charge method (FSCM) with local mesh refinement and smart calculations of elements interactions which improves the accuracy of the calculations and, at the same time, preserves the performance speed.
https://doi.org/10.1109/COMPUMAG56388.2023.10411804
Linear versus quadratic detrending in analyzing simultaneous changes in DC-EEG and transcutaneous pCO2. - In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), (2023), insges. 4 S.
Physiological direct current (DC) potential shifts in electroencephalography (EEG) can be masked by artifacts such as slow electrode drifts. To reduce the influence of these artifacts, linear detrending has been proposed as a pre-processing step. We considered quadratic detrending, which has hardly been addressed for ultralow frequency components in EEG. We compared the performance of linear and quadratic detrending in simultaneously acquired DC-EEG and transcutaneous partial pressure of carbon dioxide during two activation methods: hyperventilation (HV) and apnea (AP). Quadratic detrending performed significantly better than linear detrending in HV, while for AP, our analysis was inconclusive with no statistical significance. We conclude that quadratic detrending should be considered for DC-EEG preprocessing.
https://doi.org/10.1109/EMBC40787.2023.10340855
Entrainment and resonance effects with a new mobile audio-visual stimulation device. - In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC), (2023), insges. 4 S.
Entrainment and photic driving effects triggered by repetitive visual stimulation are long-established in clinical and therapeutic scenarios. Nonetheless, such stimulation patterns are typically bound to stationary clinical and laboratory applications. We investigated the effects of repetitive stimulation with a new dynamic auditory-visual stimulation pattern using a novel consumer-grade stimulation device for home application. Fourteen volunteers (study group) received 16 sessions of combined auditory-visual stimulation during four weeks. An additional control group (seven volunteers) received auditory-only stimulation for two sessions. From 64-channel electroencephalography recordings, we compared individual alpha peak frequencies (iAPF) between week one and week four as well as power values from the time-frequency analysis. The novel stimulation device yielded stable entrainment and resonance effects for all investigated stimulation frequencies. Both groups showed no differences in their iAPFs between weeks one and four. The power comparison suggests that there are similar entrainment and resonance effects in week one and week four within the study group. We conclude that the novel portable consumer-grade stimulation device is suitable for home-based auditory-visual stimulation leading to consistent entrainment and resonance effects over the course of 16 stimulation sessions over four weeks.
https://doi.org/10.1109/EMBC40787.2023.10341051
Dropout is NOT all you need to prevent gradient leakage. - In: 37th AAAI Conference on Artificial Intelligence (AAAI-23), (2023), S. 9733-9741
Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms.
On learning of inverse kinematics for highly redundant robots with neural networks. - In: 2023 21st International Conference on Advanced Robotics (ICAR), (2023), S. 402-408
The inverse kinematic problem for redundant robots is still difficult to solve. One approach is learning the inverse kinematic model with artificial neural networks, while the key challenge is the ambiguity of solutions. Due to the redundancy in the robot's degrees of freedom, there are multiple or even unlimited valid joint states bringing the end effector to a desired position. We show to what extent this problem influences the achievable accuracy of supervised training approaches depending on the number of degrees of freedom. To overcome the difficulties, a new training scheme is proposed, which uses the analytically solvable forward kinematics model. The new unsupervised training method uses random sampling in the joint state space and is not dependent on ambiguous tuples of joint values and end effector poses. We analyze the effect of the sample density on the remaining position error and show that additional soft constraints can easily be integrated in the training scheme, which offers the possibility to consider obstacle avoidance directly in the inverse kinematic model. Evaluations have been done using different robot models with up to 20 degrees of freedom, while not only position, but also the end effector's orientation at the goal point is considered.
https://doi.org/10.1109/ICAR58858.2023.10406939
PanopticNDT: efficient and robust panoptic mapping. - In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2023), S. 7233-7240
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT – an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application.
https://doi.org/10.1109/IROS55552.2023.10342137
Formative-Assessment Freirean-dialogue API for data-analytics trainee-teachers. - In: FIE 2023, (2023), insges. 5 S.
ISBN 979-8-3503-3642-9
This paper presents an innovative API to help a trainee-teacher design Formative-Assessment (hereafter FA) Freirean-dialogues in a data-analytics education project. The API is emerged in context of our act to help a trainee-teacher in Germany understand what hinder the Syrian oppressed students/immigrants express freely their thoughts/critiques in group-discussions about how to initialize data-analytics education project for their self-overcoming goals. The paper shows how we interpret Freire's two works “pedagogy of the oppressed [1] and pedagogy of freedom [2]” and expose our interpretations as API's design-elements and use-value guidelines for trainee teacher's two helping functions; the first is to help him/her coordinate critical reading/analyzing circles for practicing Freirean multi-factor analysis of oppression in context of the Syrian adult oppressed learners. The second is to help him/her design and prepare FA Freirean dialogues to assess formatively (i.e., analyze critically) a limit and oppressive situation of Syrian adult learners. Moreover, it enables him/her to prepare advanced FA Freirean dialogues for analyzing critically the limit and oppressive situation and its Freirean untested feasibilities for self-overcoming. To test the API's two helping functions, test cases are suggested as next steps of this work-in-progress paper. Finally, for reflective-feedback exchange with FIE community we summarize the API's limits and our next steps to improve it.
https://doi.org/10.1109/FIE58773.2023.10342969
Demo: Chat based emergency service via Long Range wireless communication (LoRa). - In: Proceedings of the 48th IEEE Conference on Local Computer Networks, (2023), insges. 3 S.
Internet and mobile communication are the foundation of today’s worldwide interaction and connectivity. Recently, several approaches have been introduced in order to add resilience to communication networks mainly by using low cost and commodity hardware by e.g., facilitating smart phones interconnected via LoRa communication. We extend those ideas by introducing a novel and versatile networking architecture for emergency communication in the context of crisis situations (e.g., when cellular coverage is gone) for mobile devices to keep communication overhead low by following an ad hoc and distributed multi-hop networking approach. In this work, we present a demo of our network to handle emergency communication. It features the long-range, wide area communication technique (LoRa), a web-based chat client with a user-friendly interface, a novel data processing approach, as well as a routing algorithm to ensure fast and efficient communication.
https://doi.org/10.1109/LCN58197.2023.10223398
Feasibility study: towards a robot-assisted gait training in ophthalmological rehabilitation. - In: 2023 International Conference on Rehabilitation Robotics (ICORR), (2023), insges. 6 S.
The idea of using mobile assistance robots for gait training in rehabilitation has been increasingly explored in recent years due to the associated benefits. This paper describes how the previous results of research and praxis on gait training with a mobile assistance robot in orthopedic rehabilitation can be transferred to ophthalmic-related orientation and mobility training for blind and visually impaired people. To this end, the specific requirements for such orientation and mobility training are presented from a therapeutic perspective. Using sensory data, it is investigated how the analysis of training errors can be automated and transferred back to the training person. These pre-examinations are the prerequisite for any form of robot-assisted mobile gait training in ophthamological rehabilitation, which does not exist so far and which is expected to be of great benefit to these patients.
https://doi.org/10.1109/ICORR58425.2023.10304760
Door manipulation as a fundamental skill realized on robots with differential drive. - In: ISR Europe 2023: 56th International Symposium on Robotics, (2023), S. 338-345
In the context of assistive mobile service robotics for elderly living in nursing homes, but also for robots realizing autonomous transport in large public buildings in general, a fundamental challenge is to overcome closed doors on their way. We review the state of the art for autonomous door opening by mobile robots and present a modular framework for enabling various robots in this task. The necessary building blocks are introduced, and evaluation results for their application on two different robot platforms are presented. A common property of our platforms, which can be found on many commercial lowcost robots is the use of differential drives. This is limiting the maneuverability and is, therefore, an important constraint for the realization of door manipulation strategies. Furthermore, our method is not dependent on computationally expensive computer vision methods but utilizes the usually available laser-range scanner for localizing and analyzing the door to be manipulated.
https://ieeexplore.ieee.org/document/10363092