@misc{2290, abstract = {— Please note: This is a revised version as the dataset contained an error in the previous version. In this version, the error in the dataset has been corrected. The old version is available here for reference: http://doi.org/10.57720/1956 — The experimental data published in this data set correspond to surface electromyographic recordings of the biceps and triceps muscles, as well as the elbow joint angle of several human subjects during the performance of various arm exercises under varying load situations. The data were originally recorded for the purpose of optimizing a musculoskeletal model of the upper arm with a focus on elbow movement in 2018 and 2019 as part of a research project at Bielefeld University of Applied Sciences, Germany. In the context of this publication, the data are made available to the scientific community.}, author = {Mechtenberg, Malte and Grimmelsmann, Nils and Meyer, Hanno Gerd and Schneider, Axel}, keywords = {sEMG, surface electromyography, biceps, triceps, data set}, publisher = {FH Bielefeld}, title = {{Surface electromyographic recordings of the biceps and triceps brachii for various postures, motion velocities and load conditions}}, doi = {10.57720/2290}, year = {2023}, } @article{3328, abstract = { Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC). }, author = {Mechtenberg, Malte and Schneider, Axel}, issn = {1662-5218}, journal = {Frontiers in Neurorobotics}, publisher = {Frontiers Media SA}, title = {{A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model}}, doi = {10.3389/fnbot.2023.1179224}, volume = {17}, year = {2023}, } @inproceedings{3777, abstract = {This work presents a study that compares the angular accuracy of a binaural bioinspired method with classical delay-and-sum beamforming using linear acoustic arrays with an increasing number of sensors. In addition, this study also focuses on the frequency band dependent angular accuracy of the bioinspired method, as previous work has optimized it only for localizing narrowband sound sources. Therefore, a comparison of the localization accuracy for narrowband and broadband excitatory sounds is presented. This enables conclusions about the potential to reduce resources in terms of sensor array spatial requirements and the number of sensors if a bioinspired system is used.}, author = {Jünemann, Philipp and Mechtenberg, Malte and Schneider, Axel and Waßmuth, Joachim}, booktitle = {Engineering for a Changing World : Proceedings; 60th ISC, Ilmenau Scientific Colloquium}, publisher = {ilmedia}, title = {{Comparative study of a bioinspired sound source localization algorithm and a standard beamformer}}, doi = {10.22032/DBT.59141}, year = {2023}, } @article{3765, abstract = { Abstract - Bioinspired methods for sound source localization offer opportunities for resource reduction as well as concurrent performance improvement in contrast to conventional techniques. Usually, sound source localization requires a large number of microphones arranged in irregular geometries, and thus has high resource requirements in terms of space and data processing. Motivated by biology and using digital signal processing methods, an approach that adapts the coupled hearing system of the fly Ormia ochracea with a minimally distant two-microphone array is presented. Despite its physiology, the fly is able to overcome physical limitations in localizing low-frequency sound sources. By exploiting the filtering effect of the coupling system, the direction-of-arrival of the sound is determined with two microphones at an intermediate distance of 0.06 m. For conventional beamforming algorithms, these physical limitations would result in degraded localization performance. In this work, the bioinspired coupling system is analyzed and subsequently parameterized direction-sensitive for different directions of incidence of the sound. For the parameterization, an optimization method is presented which can be adopted for excitation with plane as well as spherical sound wave propagation. Finally, the method was assessed using simulated and measured data. For 90% of the simulated scenarios, the correct direction of incidence could be determined with an accuracy of less than 1 ∘ despite the use of a minimal distant two-microphone array. The experiments with measured data also resulted in a correct determination of the direction of incidence, which qualifies the bioinspired method for practical use in digital hardware systems. }, author = {Jünemann, Philipp and Schneider, Axel and Waßmuth, Joachim}, issn = {1748-3190}, journal = {Bioinspiration & Biomimetics}, number = {5}, publisher = {IOP Publishing}, title = {{Direction-of-arrival estimation for acoustic signals based on direction-dependent parameter tuning of a bioinspired binaural coupling system}}, doi = {10.1088/1748-3190/ace50a}, volume = {18}, year = {2023}, } @article{3453, abstract = { For assistive devices such as active orthoses, exoskeletons or other close-to-body robotic-systems, the immediate prediction of biological limb movements based on biosignals in the respective control system can be used to enable intuitive operation also by untrained users e.g. in healthcare, rehabilitation or industrial scenarios. Surface electromyography (sEMG) signals from the muscles that drive the limbs can be measured before the actual movement occurs and, hence, can be used as source for predicting limb movements. The aim of this work was to create a model that can be adapted to a new user or movement scenario with little measurement and computing effort. Therefore, a biomechanical model is presented that predicts limb movements of the human forearm based on easy to measure sEMG signals of the main muscles involved in forearm actuation ( lateral and long head of triceps and short and long head of biceps ). The model has 42 internal parameters of which 37 were attributed to 8 individually measured physiological measures (location of acromion at the shoulder, medial/lateral epicondyles as well as olecranon at the elbow, and styloid processes of radius/ulna at the wrist; maximum muscle forces of biceps and triceps ). The remaining 5 parameters are adapted to specific movement conditions in an optimization process. The model was tested in an experimental study with 31 subjects in which the prediction quality of the model was assessed. The quality of the movement prediction was evaluated by using the normalized mean absolute error (nMAE) for two arm postures (lower, upper), two load conditions (2 kg, 4 kg) and two movement velocities (slow, fast). For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons. This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality. }, author = {Grimmelsmann, Nils and Mechtenberg, Malte and Schenck, Wolfram and Meyer, Hanno Gerd and Schneider, Axel}, issn = {1932-6203}, journal = {PLOS ONE}, number = {8}, publisher = {Public Library of Science (PLoS)}, title = {{sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters}}, doi = {10.1371/journal.pone.0289549}, volume = {18}, year = {2023}, } @inbook{3055, author = {Simmering, Janneke and Hermes, Luca and Schneider, Axel and Schilling, Malte}, booktitle = {Robotics in Natural Settings. CLAWAR 2022}, editor = {Cascalho, José M. and Tokhi, Mohammad Osman and Silva, Manuel F. and Mendes, Armando and Goher, Khaled and Funk, Matthias}, isbn = {978-3-031-15225-2}, issn = {2367-3389}, location = {Ponta Delgada. Azores, Portugal}, pages = {264--275}, publisher = {Springer International Publishing}, title = {{Adaptation of a Decentralized Controller to Curve Walking in a Hexapod Robot}}, doi = {10.1007/978-3-031-15226-9_26}, year = {2023}, } @misc{1956, abstract = {— Please note: The dataset in this version contains an error. See the separate text file for more details. A revised version including the corrected data is available here: http://doi.org/10.57720/2290 — The experimental data published in this data set correspond to surface electromyographic recordings of the biceps and triceps muscles, as well as the elbow joint angle of several human subjects during the performance of various arm exercises under varying load situations. The data were originally recorded for the purpose of optimizing a musculoskeletal model of the upper arm with a focus on elbow movement in 2018 and 2019 as part of a research project at Bielefeld University of Applied Sciences, Germany. In the context of this publication, the data are made available to the scientific community.}, author = {Mechtenberg, Malte and Grimmelsmann, Nils and Meyer, Hanno Gerd and Schneider, Axel}, keywords = {sEMG, surface electromyography, biceps, triceps, data set}, publisher = {FH Bielefeld}, title = {{Surface electromyographic recordings of the biceps and triceps brachii for various postures, motion velocities and load conditions}}, doi = {10.57720/1956}, year = {2022}, } @misc{2175, abstract = {

This publication contains data which was recorded during an experimental measurement campaign and was used to train a model of the biceps brachii distal tendon to predict myotendinous junction displacement over varying joint angles and tendon forces.

Further Information about the data set is contained within the README.pdf file.

 

Acknowledgements

This work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, https://www.dfg.de/en/) - ref. no. SCHN 1339/3-1, by the Federal Ministry of Education and Research (BMBF) within the project ITS.ML - ID 01IS18041 A (AS) and by the research training Group "DataNinja" funded by the German federal state of North Rhine-Westphalia.

}, author = {Mechtenberg, Malte and Grimmelsmann, Nils and Meyer, Hanno Gerd and Schneider, Axel}, keywords = {biceps brachii distal tendon displacement, biceps brachii distal tendon, myotendinous junction, biceps brachii, 2-D ultrasound videos, ultrasound, tendon}, publisher = {FH Bielefeld}, title = {{2-D ultrasound videos of the distal biceps brachii myotendinous junction displacement over varying elbow angles and tendon loads}}, doi = {10.5281/zenodo.7081639}, year = {2022}, } @article{2174, abstract = { Tendons consist of passive soft tissue with non linear material properties. They play a key role in force transmission from muscle to skeletal structure. The properties of tendons have been extensively examined in vitro . In this work, a non linear model of the distal biceps brachii tendon was parameterized based on measurements of myotendinous junction displacements in vivo at different load forces and elbow angles. The myotendinous junction displacement was extracted from ultrasound B-mode images within an experimental setup which also allowed for the retrieval of the exerted load forces as well as the elbow joint angles. To quantify the myotendinous junction movement based on visual features from ultrasound images, a manual and an automatic method were developed. The performance of both methods was compared. By means of exemplary data from three subjects, reliable fits of the tendon model were achieved. Further, different aspects of the non linear tendon model generated in this way could be reconciled with individual experiments from literature. }, author = {Mechtenberg, Malte and Grimmelsmann, Nils and Meyer, Hanno Gerd and Schneider, Axel}, issn = {1932-6203}, journal = {PLOS ONE}, number = {10}, publisher = {Public Library of Science (PLoS)}, title = {{Manual and semi-automatic determination of elbow angle-independent parameters for a model of the biceps brachii distal tendon based on ultrasonic imaging}}, doi = {10.1371/journal.pone.0275128}, volume = {17}, year = {2022}, } @inproceedings{3050, abstract = {Soft sensors combine a hardware component with an intelligent algorithmic processing of the raw sensor signals. While individualization of software components according to a person’s specific needs is comparably cheap, individualization of the sensor hardware itself is usually impossible in mass production. At the same time, the number of raw sensors should be minimum to reduce production costs. In this contribution, we propose to model this challenge as a feature selection problem, which optimizes a feature set simultaneously with respect to a family of functions corresponding to individualized post-processing of sensor signals. This concept is integrated into a number of different classical feature selection schemes, and evaluated in the context of the placement of pressure sensors as part of a shoe insole. It turns out that feature selection respecting the class of functions is superior to both placement based on anatomical considerations and classical feature selection methods.}, author = {Vieth, Markus and Grimmelsmann, Nils and Schneider, Axel and Hammer, Barbara}, booktitle = {Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings}, editor = {Yin, Hujun and Camacho, David and Tino, Peter}, isbn = {978-3-031-21752-4}, issn = {1611-3349}, location = {Manchester}, pages = {326--337}, publisher = {Springer International Publishing}, title = {{Efficient Sensor Selection for Individualized Prediction Based on Biosignals}}, doi = {10.1007/978-3-031-21753-1_32}, volume = {13756}, year = {2022}, } @article{3049, author = {Schilling, Malte and Paskarbeit, Jan and Ritter, Helge and Schneider, Axel and Cruse, Holk}, issn = {1941-0468}, journal = {IEEE Transactions on Robotics}, number = {2}, pages = {666--682}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, title = {{From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker}}, doi = {10.1109/TRO.2021.3106832}, volume = {38}, year = {2022}, } @article{2567, abstract = {Biological beings perceive different signals from their environment through their sensory organs to estimate object size and position. In this work, the aspect of object localization and size is investigated in a detailed simulation study based on a simplified, analytical model of a weakly electric fish. These fishes generate a three-dimensional dipole-like electric field for object localization and communication. With the abstracted model of this bio-template, different features of a voltage profile that is obtained from an assumed sensor line (representing the voltage-sensitive skin of fish) were identified for the determination of object size and position. These features were categorized according to their suitability for measurement scenarios with a stationary or moving sensor line. On the one hand, some features are considered individually to obtain information about the object size and position, and on the other hand, a combination of these features create a distance measure which is independent of the object size.}, author = {Hunke, Kevin and Engelmann, Jacob and Schneider, Axel}, issn = {1941-0123}, journal = {IEEE Instrumentation & Measurement Magazine}, number = {9}, pages = {10--18}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, title = {{Feature Extraction from a Static and a Moving One-Dimensional Voltage Sensor Line for the Electric Field-Based Determination of Object Size and Position in Aqueous Media}}, doi = {10.1109/MIM.2022.9955466}, volume = {25}, year = {2022}, } @article{1730, abstract = { Limb movement prediction based on surface electromyography (sEMG) for the control of wearable robots, such as active orthoses and exoskeletons, is a promising approach since it provides an intuitive control interface for the user. Further, sEMG signals contain early information about the onset and course of limb movements for feedback control. Recent studies have proposed machine learning-based modeling approaches for limb movement prediction using sEMG signals, which do not necessarily require domain knowledge of the underlying physiological system and its parameters. However, there is limited information on which features of the measured sEMG signals provide the best prediction accuracy of machine learning models trained with these data. In this work, the accuracy of elbow joint movement prediction based on sEMG data using a simple feedforward neural network after training with different single- and multi-feature sets and data segmentation parameters was compared. It was shown that certain combinations of time-domain and frequency-domain features, as well as segmentation parameters of sEMG data, improve the prediction accuracy of the neural network as compared to the use of a standard feature set from the literature. }, author = {Leserri, David and Grimmelsmann, Nils and Mechtenberg, Malte and Meyer, Hanno Gerd and Schneider, Axel}, issn = {2227-7390}, journal = {Mathematics}, number = {6}, publisher = {MDPI AG}, title = {{Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network}}, doi = {10.3390/math10060932}, volume = {10}, year = {2022}, } @article{3600, abstract = {This a secondary publication from 2023 of an original article published in 2021. Parallax, as a visual effect, is used for depth perception of objects. But is there also the effect of parallax in the context of electric field imagery? In this work, the example of weakly electric fish is used to investigate how the self-generated electric field that these fish utilize for orientation and communication alike, may be used as a template to define electric parallax. The skin of the electric fish possesses a vast amount of electroreceptors that detect the self-emitted dipole-like electric field. In this work, the weakly electric fish is abstracted as an electric dipole with a sensor line in between the two emitters. With an analytical description of the object distortion for a uniform electric field, the distortion in a dipole-like field is simplified and simulated. On the basis of this simulation, the parallax effect could be demonstrated in electric field images i.e. by closer inspection of voltage profiles on the sensor line. Therefore, electric parallax can be defined as the relative movement of a signal feature of the voltage profile (here, the maximum or peak of the voltage profile) that travels along the sensor line peak trace (PT). The PT width correlates with the object’s vertical distance to the sensor line, as close objects create a large PT and distant objects a small PT, comparable with the effect of visual motion parallax. }, author = {Hunke, Kevin and Engelmann, Jacob and Meyer, Hanno Gerd and Schneider, Axel}, issn = {1748-3190}, journal = {Bioinspiration & Biomimetics}, number = {1}, publisher = {IOP Publishing}, title = {{Motion parallax for object localization in electric fields}}, doi = {10.57720/3600}, volume = {17}, year = {2021}, } @article{1698, abstract = {Parallax, as a visual effect, is used for depth perception of objects. But is there also the effect of parallax in the context of electric field imagery? In this work, the example of weakly electric fish is used to investigate how the self-generated electric field that these fish utilize for orientation and communication alike, may be used as a template to define electric parallax. The skin of the electric fish possesses a vast amount of electroreceptors that detect the self-emitted dipole-like electric field. In this work, the weakly electric fish is abstracted as an electric dipole with a sensor line in between the two emitters. With an analytical description of the object distortion for a uniform electric field, the distortion in a dipole-like field is simplified and simulated. On the basis of this simulation, the parallax effect could be demonstrated in electric field images i.e. by closer inspection of voltage profiles on the sensor line. Therefore, electric parallax can be defined as the relative movement of a signal feature of the voltage profile (here, the maximum or peak of the voltage profile) that travels along the sensor line peak trace (PT). The PT width correlates with the object’s vertical distance to the sensor line, as close objects create a large PT and distant objects a small PT, comparable with the effect of visual motion parallax. }, author = {Hunke, Kevin and Engelmann, Jacob and Meyer, Hanno Gerd and Schneider, Axel}, issn = {1748-3190}, journal = {Bioinspiration & Biomimetics}, number = {1}, publisher = {IOP Publishing}, title = {{Motion parallax for object localization in electric fields}}, doi = {10.1088/1748-3190/ac3215}, volume = {17}, year = {2021}, } @article{1201, author = {Shah, Zafran Hussain and Müller, Marcel and Wang, Tung-Cheng and Scheidig, Philip Maurice and Schneider, Axel and Schüttpelz, Mark and Huser, Thomas and Schenck, Wolfram}, issn = {2327-9125}, journal = {Photonics Research}, number = {5}, publisher = {The Optical Society}, title = {{Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images}}, doi = {10.1364/PRJ.416437}, volume = {9}, year = {2021}, } @article{1183, author = {Paskarbeit, Jan and Beyer, Simon and Engel, Matthäus and Gucze, Adrian and Schröder, Johann and Schneider, Axel}, issn = {09218890}, journal = {Robotics and Autonomous Systems}, publisher = {Elsevier BV}, title = {{Ourobot—A sensorized closed-kinematic-chain robot for shape-adaptive rolling in rough terrain}}, doi = {10.1016/j.robot.2020.103715}, volume = {140}, year = {2021}, } @techreport{2778, abstract = { Abstract - Super-resolution structured illumination microscopy (SR-SIM) provides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change. }, author = {Shah, Zafran Hussain and Müller, Marcel and Wang, Tung-Cheng and Scheidig, Philip Maurice and Schneider, Axel and Schüttpelz, Mark and Huser, Thomas and Schenck, Wolfram}, publisher = {Cold Spring Harbor Laboratory}, title = {{Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images}}, doi = {https://doi.org/10.1101/2020.10.27.352633}, year = {2020}, } @article{1699, author = {Meyer, Hanno Gerd and Klimeck, Daniel and Paskarbeit, Jan and Rückert, Ulrich and Egelhaaf, Martin and Porrmann, Mario and Schneider, Axel}, issn = {1932-6203}, journal = {PLOS ONE}, number = {4}, publisher = {Public Library of Science (PLoS)}, title = {{Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR}}, doi = {10.1371/journal.pone.0230620}, volume = {15}, year = {2020}, } @article{3052, abstract = {Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.}, author = {Dürr, Volker and Arena, Paolo P. and Cruse, Holk and Dallmann, Chris J. and Drimus, Alin and Hoinville, Thierry and Krause, Tammo and Mátéfi-Tempfli, Stefan and Paskarbeit, Jan and Patanè, Luca and Schäffersmann, Mattias and Schilling, Malte and Schmitz, Josef and Strauss, Roland and Theunissen, Leslie and Vitanza, Alessandra and Schneider, Axel}, issn = {1662-5218}, journal = {Frontiers in Neurorobotics}, number = {6}, publisher = {Frontiers Media SA}, title = {{Integrative Biomimetics of Autonomous Hexapedal Locomotion}}, doi = {10.3389/fnbot.2019.00088}, volume = {13}, year = {2019}, }