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Advancing knee adduction moment prediction for neuromuscular training via functional joint definitions and real–time simulation using OpenSim (2025)
Goell, Fabian ; Braunstein, Björn ; Stemmler, Maike ; Fasse, Alessandro ; Abel, Dirk ; Albracht, Kirsten
Neuromuscular training to strengthen leg muscles is an important part of the treatment of musculoskeletal disorders and chronic diseases and preventing age–related muscle loss. This study evaluates different individualization approaches and their real–time implementation for OpenSim musculoskeletal models to estimate the external knee adduction moment during a leg–press exercise. A robotic neuromuscular training platform was utilized to perform isometric and dynamic leg extension exercises. Data were collected for 13 subjects using a 3D motion capture system and force plate measurements from the robotic training platform. Functional joint parameters, determined through dynamic reference movements, were integrated into the OpenSim models, allowing a personalized representation of the hip, knee, and ankle joints. This integration was compared with a conventional scaling method. The results indicate that the incorporation of functional joint axes can significantly enhance the accuracy of biomechanical simulations. These methods provide a real–time and a more precise estimate of the external knee adduction moment compared to conventional scaling approaches and underscore the importance of individualized model parameters in biomechanical research.
Anpassung der Halswirbelsäule in der Schwerelosigkeit und das Risiko von Bandscheibenvorfällen (2025)
Belavy, Daniel ; Ambrecht, Gabriella ; Albracht, Kirsten ; Brisby, Helena ; Falla, Deborah ; Scheuring, Richard ; Sovelius, Roope ; Wilke, Hans-Joachim ; Greiner-Perth, Ann-Kathrin ; Vogt, Morten ; Liebsch, Christian ; Rennerfelt, Kajsa ; Martinez-Valdes, Eduardo ; Arvanitidis, Michail ; Goell, Fabian ; Braunstein, Björn ; Kaczorowski, Svenja ; Arora, Nitin Kumar ; Teichert, Florian ; Schüngel, Verena ; Moreira, Eva
Astronaut*innen haben ein etwa 20-mal höheres Risiko für Bandscheibenvorfälle (BSV) in der Halswirbelsäule (HWS) als Menschen auf der Erde. Die genauen Ursachen sind noch unklar. Durch die Untersuchung der Bindegewebe-, Muskel- und Knochenadaption der HWS vor und nach dem Aufenthalt im Weltall sollen die Mechanismen aufgeklärt werden, die das erhöhte Risiko von BSV in der HWS begünstigen.
Real-time EEG-based BCI for self-paced motor imagery and motor execution using functional neutral networks (2025)
Heim, Mavin ; Heinrichs, Florian ; Hueppe, Michael ; Nunez, Fran ; Szameitat, Alexander ; Reuter, Murial ; Goetz, Stefan M. ; Weber, Corinna
This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and motor imagery (MI). FNNs represent a neural network architecture tailored to smooth processes, and as such have been applied to EEG data classification recently. The paper proposes a comprehensive pipeline encompassing data acquisition, synchronization, pre-processing, training of FNNs, and real-time inference to enable the seamless integration of FNNs into real-world BCI applications. For the first time, FNNs are integrated into an end-to-end pipeline and serve for live inference outside a strict laboratory setting. In pursuit of a more accessible electroencephalography (EEG) artificial intelligence (AI) training scenario, the paper introduces a self-paced environment for auto-labeling EEG data. A customdesigned Pong game serves as the training task and enables subjects to engage in MI/ME tasks while receiving immediate visual feedback. To automate the labeling process of the recorded EEG data, the movements of both arms are captured with inertial measurement units (IMUs) and analyzed through gesture recognition. This novel training framework contributes to more natural and engaging data collection and reduces pre-processing for model training. To provide a comprehensive evaluation, the paper compares the performance of FNN and EEGNet in the self-paced MI/ME tasks. The comparative analysis addresses factors such as classification accuracy, real-time processing speed, and power consumption. Furthermore, the study explores various auto-labeling methods within the self-paced environment, analyzing their impact on the classification performances of both architectures. By evaluating these labeling methods, this work addresses the challenge of accurate and efficient EEG data labeling, crucial for training robust models for prediction of time critical events. The proposed pipeline and experimental design culminate in a full-scale evaluation of the FNN-based classification system to demonstrate its efficacy in real-time MI/ME tasks. The paper’s contributions not only establish FNNs as a potent tool for EEG classification but also provide valuable insights into enhancing the accessibility, usability, and performance of EEG-based AI systems in real-world applications.
Preprint: Monitoring machine learning models: online detection of relevant deviations (2023)
Heinrichs, Florian
Machine learning models are essential tools in various domains, but their performance can degrade over time due to changes in data distribution or other factors. On one hand, detecting and addressing such degradations is crucial for maintaining the models' reliability. On the other hand, given enough data, any arbitrary small change of quality can be detected. As interventions, such as model re-training or replacement, can be expensive, we argue that they should only be carried out when changes exceed a given threshold. We propose a sequential monitoring scheme to detect these relevant changes. The proposed method reduces unnecessary alerts and overcomes the multiple testing problem by accounting for temporal dependence of the measured model quality. Conditions for consistency and specified asymptotic levels are provided. Empirical validation using simulated and real data demonstrates the superiority of our approach in detecting relevant changes in model quality compared to benchmark methods. Our research contributes a practical solution for distinguishing between minor fluctuations and meaningful degradations in machine learning model performance, ensuring their reliability in dynamic environments
A distribution free test for changes in the trend function of locally stationary processes (2021)
Heinrichs, Florian ; Dette, Holger
In the common time series model Xi,n = μ(i/n) + εi,n with non-stationary errors we consider the problem of detecting a significant deviation of the mean function μ from a benchmark g(μ) (such as the initial value μ(0) or the average trend f 1 0 μ(t)dt). The problem is motivated by a more realistic modelling of change point analysis, where one is interested in identifying relevant deviations in a smoothly varying sequence of means (μ(i/n))i=1,...,n and cannot assume that the sequence is piecewise constant. A test for this type of hypotheses is developed using an appropriate estimator for the integrated squared deviation of the mean function and the threshold. By a new concept of self-normalization adapted to nonstationary processes an asymptotically pivotal test for the hypothesis of a relevant deviation is constructed. The results are illustrated by means of a simulation study and a data example.
Preprint: Consumer-grade EEG-based eye tracking (2025)
Vasconcelos Afonso, Tiago ; Heinrichs, Florian
Electroencephalography-based eye tracking (EEG-ET) leverages eye movement artifacts in EEG signals as an alternative to camera-based tracking. While EEG-ET offers advantages such as robustness in low-light conditions and better integration with brain-computer interfaces, its development lags behind traditional methods, particularly in consumer-grade settings. To support research in this area, we present a dataset comprising simultaneous EEG and eye-tracking recordings from 113 participants across 116 sessions, amounting to 11 hours and 45 minutes of recordings. Data was collected using a consumer-grade EEG headset and webcam-based eye tracking, capturing eye movements under four experimental paradigms with varying complexity. The dataset enables the evaluation of EEG-ET methods across different gaze conditions and serves as a benchmark for assessing feasibility with affordable hardware. Data preprocessing includes handling of missing values and filtering to enhance usability. In addition to the dataset, code for data preprocessing and analysis is available to support reproducibility and further research.
Detecting 5G narrowband jammers with CNN, k-nearest neighbors, and support vector machines (2024)
Varotto, Matteo ; Heinrichs, Florian ; Schürg, Timo ; Tomasin, Stefano ; Valentin, Stefan
5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning (ML). We propose to detect jamming at the physical layer with an ML model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.
Preprint: OML-AD: online machine learning for anomaly detection in time series data (2024)
Wette, Sebastian ; Heinrichs, Florian
Time series are ubiquitous and occur naturally in a variety of applications -- from data recorded by sensors in manufacturing processes, over financial data streams to climate data. Different tasks arise, such as regression, classification or segmentation of the time series. However, to reliably solve these challenges, it is important to filter out abnormal observations that deviate from the usual behavior of the time series. While many anomaly detection methods exist for independent data and stationary time series, these methods are not applicable to non-stationary time series. To allow for non-stationarity in the data, while simultaneously detecting anomalies, we propose OML-AD, a novel approach for anomaly detection (AD) based on online machine learning (OML). We provide an implementation of OML-AD within the Python library River and show that it outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.
Detecting changes in locally stationary time series (2020)
Heinrichs, Florian
Das Entdecken struktureller Veränderungen in Zeitreihen ist eine der wichtigsten Herausforderungen der modernen Statistik, da nur so eine zuverlässige statistische Inferenz gewährleistet werden kann. Nach einer kurzen Einleitung in die mathematischen Grundlagen, werden in dieser Arbeit drei Testverfahren entwickelt, mit denen Zeitreihen auf Stationarität untersucht werden können. Der erste Test basiert auf einer CUSUM-Statistik und testet die Annahme der schwachen Stationarität funktionaler Zeitreihen gegen die Alternative gradueller Veränderungen. Die anderen beiden Tests erkennen relevante Abweichungen in Erwartungswerten reeller Zeitreihen, die sich stetig über die Zeit entwickeln dürfen. Der eine dieser beiden Tests definiert eine Abweichung als relevant, falls die maximale Abweichung von einem Vergleichswert groß ist, wohingegen der andere Test den L2 Abstand betrachtet.
Preprint: EEG-EyeTrack: a benchmark for time series and functional data analysis with open challenges and baselines (2025)
Vasconcelos Afonso, Tiago ; Heinrichs, Florian
A new benchmark dataset for functional data analysis (FDA) is presented, focusing on the reconstruction of eye movements from EEG data. The contribution is twofold: first, open challenges and evaluation metrics tailored to FDA applications are proposed. Second, functional neural networks are used to establish baseline results for the primary regression task of reconstructing eye movements from EEG signals. Baseline results are reported for the new dataset, based on consumer-grade hardware, and the EEGEyeNet dataset, based on research-grade hardware.
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