arXiv subjects

Heiko Oppel

Publications and source records attributed to Heiko Oppel.

6 recordsLinked to original sources

arXiv2026

Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation

Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs). Such systems assign each repetition to a category, yet a relevant share of repetitions falls near a class boundary, where even trained raters disagree. Classifiers trained with one-hot labels collapse these borderline repetitions onto a single class and discard this ambiguity. We address this with a method that automatically generates a label distribution per repetition without a large rater pool. We train a network to reproduce the full distribution with a Kullback-Leibler objective, the ambiguity approach, and compare it against a one-hot cross-entropy baseline on four IMU exercise datasets. From the network output we further determine whether a repetition is ambiguous and which classes are relevant to it. The ambiguity approach matched or exceeded the baseline classification on all four datasets, and detected ambiguity and the relevant classes more reliably. Representing the label distribution in the training target therefore adds information about ambiguity at no cost to classification.

cs.LG↗

arXiv2025

Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models

Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model. Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks.

cs.LG↗

arXiv2025

GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors

Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated classification models for robust assessment of physiotherapeutic exercise and gait analysis requires large, diverse datasets that are costly and time-consuming to collect. We present a multimodal dataset of physiotherapeutic and gait-related exercises, including correct and clinically relevant variants, recorded from 19 healthy subjects using synchronized IMUs and optical marker-based motion capture (MoCap). It contains data from nine IMUs and 68 markers tracking full-body kinematics. Four markers per IMU allow direct comparison between IMU- and MoCap-derived orientations. We additionally provide processed IMU orientations aligned to common segment coordinate systems, subject-specific OpenSim models, inverse kinematics outputs, and visualization tools for IMU-derived orientations. The dataset is fully annotated with movement quality ratings and timestamped segmentations. It supports various machine learning tasks such as exercise evaluation, gait classification, temporal segmentation, and biomechanical parameter estimation. Code for postprocessing, alignment, inverse kinematics, and technical validation is provided to promote reproducibility.

cs.CV↗

arXiv2025

Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation

Automated evaluation of movement quality can enhance physiotherapeutic treatment and sports training by providing objective, real-time feedback. However, deep learning models that assess movements captured by inertial measurement units (IMUs) are often limited by data scarcity, class imbalance, and label ambiguity. We present a data augmentation method that generates IMU data using musculoskeletal simulations integrated with systematic modifications of movement trajectories. The approach enforces anatomically plausible kinematic constraints and enables automatic labeling by combining inverse kinematic parameters with a knowledge-based evaluation strategy. Across four datasets of varying complexity, augmented variants closely resemble real-world data and contribute to gains in classification accuracy, generalization to unseen subjects, and patient-specific fine-tuning from few examples. The magnitude of these gains varies with dataset properties, in particular class balance and label ambiguity. These findings indicate that musculoskeletal simulation-based augmentation can address common challenges faced by deep learning applications in physiotherapeutic exercise evaluation.

cs.LG↗

arXiv2025

Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs

Denoising diffusion probabilistic models are able to generate synthetic sensor signals. The training process of such a model is controlled by a loss function which measures the difference between the noise that was added in the forward process and the noise that was predicted by the diffusion model. This enables the generation of realistic data. However, the randomness within the process and the loss function itself makes it difficult to estimate the quality of the data. Therefore, we examine multiple similarity metrics and adapt an existing metric to overcome this issue by monitoring the training and synthetisation process using those metrics. The adapted metric can even be fine-tuned on the input data to comply with the requirements of an underlying classification task. We were able to significantly reduce the amount of training epochs without a performance reduction in the classification task. An optimized training process not only saves resources, but also reduces the time for training generative models.

cs.LG↗

arXiv2024

IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems

Kinematic sensors are often used to analyze movement behaviors in sports and daily activities due to their ease of use and lack of spatial restrictions, unlike video-based motion capturing systems. Still, the generation, and especially the labeling of motion data for specific activities can be time-consuming and costly. Additionally, many models struggle with limited data, which limits their performance in recognizing complex movement patterns. To address those issues, generating synthetic data can help expand the diversity and variability. In this work, we propose IMUDiffusion, a probabilistic diffusion model specifically designed for multivariate time series generation. Our approach enables the generation of high-quality time series sequences which accurately capture the dynamics of human activities. Moreover, by joining our dataset with synthetic data, we achieve a significant improvement in the performance of our baseline human activity classifier. In some cases, we are able to improve the macro F1-score by almost 30%. IMUDiffusion provides a valuable tool for generating realistic human activity movements and enhance the robustness of models in scenarios with limited training data.

cs.LG↗