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The domain of human activity recognition is able to differentiate between human movements based on sensory driven systems, e.g. in the form of an IMU. Though, in order to perform those differentiation tasks, a measurement setup has to be established and subjects have to be recorded. As this is a time and cost consuming process, research groups are focused to synthetically generate data resembling human movements to improve the underlying recognition task. So far, work groups are able to generate univariate and multivariate synthetic sequences on basis of an accelerometer or six axis IMU. Yet, they lack in generalizing on unseen subjects and are not able to expand further than a single six axis IMU. In this paper, we aim to fill this gap by using the backbone of a denoising diffusion probabilistic model from the vision domain to synthetically generate multiple IMUs which are able to generalize on unseen participants. The model was adapted to fulfill the criteria of generating meaningful human motion sequences. We then evaluated the quality of the data in two ways: (1) by a subjective visual analysis with the help of a clustering approach new to this domain and (2) by the classifier improvement when adding synthetic samples. The results show a significant improvement in the classification task when synthetic samples were added to the pool of training data. One of the key findings is the benefit of improvement, even in a scarce data set of only 2 samples per subject. This is a huge advantage in the domain of HAR as it reduces the time of a subject to perform a task.

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely on deep learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 inertial measurement units, a dataset of four Functional Movement Screening exercises is recorded. Exercise execution is evaluated by physiotherapists using the Functional Movement Screening criteria. This dataset is used to train a neural network that assigns the correct Functional Movement Screening score to an exercise repetition. We use an architecture consisting of convolutional, long-short-term memory and dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform an extensive hyperparameter optimization. In addition, we are comparing different convolutional neural network structures that have been specifically adapted for use with inertial measurement data. To test the developed approach, it is trained on the data from different Functional Movement Screening exercises and the performance is compared on unknown data from known and unknown subjects. (3) Results: The evaluation shows that the presented approach is able to classify unknown repetitions correctly. However, the trained network is yet unable to achieve consistent performance on the data of previously unknown subjects. Additionally, it can be seen that the performance of the network differs depending on the exercise it is trained for. (4) Conclusions: The present work shows that the presented deep learning approach is capable of performing complex motion analytic tasks based on inertial measurement unit data. The observed performance degradation on the data of unknown subjects is comparable to publications of other research groups that relied on classical machine learning methods. However, the presented approach can rely on transfer learning methods, which allow to retrain the classifier by means of a few repetitions of an unknown subject. Transfer learning methods could also be used to compensate for performance differences between exercises.

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Human monitoring systems are already utilized in various fields like assisted living, healthcare or sport and fitness. They are able to support in everyday life or act as a pre-warning system. We developed a system to monitor the ascent of a sport climber. It is integrated in a belay device. This paper presents the first time series analysis regarding the fall of a climber utilizing such a system. A Convolutional Neural Network handles the feature engineering part of the sensor information as well as the classification of the task at hand. In this way, the time is implicitly considered by the network. An analysis regarding the size of the time window was carried out with a focus on exploring the respective results. The neural network models were then tested against an already-existing principle based on a mechanical mechanism. We show that the size of the time window is a decisive factor in a time critical system. Depending on the size of the window, the mechanical principle was able to outperform the neural network. Nevertheless, most of our models outperformed the basic principle and returned promising results in predicting the fall of a climber within up to 91.8 ms.

In shearography-based tire testing, so-called “Mode Hops”, abrupt phase changes caused by laser mode changes, can lead to significant disturbances in the interference image analysis. These artifacts distort defect assessment, lead to retesting or false-positive decisions, and, thus, represent a significant hurdle for the automation of the shearography-based tire inspection process. This work proposes a deep learning workflow that combines a pretrained, optimized ResNet-50 classifier with Grad-CAM, providing a practical and explainable solution for the reliable detection and localization of Mode Hops in shearographic tire inspection images. We trained the algorithm on an extensive, cross-machine dataset comprising more than 6.5 million test images. The final deep learning model achieves a classification accuracy of 99.67%, a false-negative rate of 0.48%, and a false-positive rate of 0.24%. Applying a probability-based quadrant-repeat decision rule within the inspection process effectively reduces process-level false positives to zero, with an estimated probability of repetition of ≤0.084%. This statistically validated approach increases the overall inspection accuracy to 99.83%. The method allows the robust detection and localization of relevant Mode Hops and represents a significant contribution to explainable, AI-supported tire testing. It fulfills central requirements for the automation of shearography-based tire testing and contributes to the possible certification process of non-destructive testing methods in safety-critical industries.

Background Intermuscular adipose tissue (IMAT) and Intramuscular fat (IMF) in skeletal muscle are critical biomarkers associated with functional decline in various musculoskeletal disorders. Routine clinical magnetic resonance imaging (MRI) sequences are frequently employed to quantify IMAT/IMF due to their broad availability and non-invasive nature. However, methodological standardization and comprehensive validation against quantitative MRI (qMRI) reference standards remain sparse. The lack of standardization and validation presents a significant barrier to clinical adoption. Furthermore, automation of IMAT/IMF quantification methods remains underexplored, which limits reproducibility and large-scale application in clinical settings. Addressing these gaps, which is the objective of this systematic review, is essential for ensuring seamless integration of IMAT/IMF quantification into clinical routine assessments. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically reviewed 65 studies that assessed IMAT or IMF using conventional MRI. The selected studies were categorized based on their methodological approaches, anatomical regions analyzed, and validation against qMRI reference standards. Additionally, we classified the level of automation of these methods and identified the necessary steps to be implemented in order to reach full automation. Results Our findings reveal a high methodological diversity in the literature, with substantial variations based on the anatomical region studied. Very few studies validated their findings against qMRI reference standards, a crucial step for establishing these methods in clinical practice. The automation potential of the reviewed methods varied significantly, with only a limited number of studies addressing full automation. Conclusion This systematic review highlights gaps in validation and automation of IMAT/IMF quantification methods using conventional MRI sequences. We provide guidance for researchers and clinicians aiming to implement these techniques in routine assessments. Transitioning from qualitative to quantitative MRI assessments requires standardization and automation to improve reproducibility and clinical applicability. Automation plays a key role in integrating these methods into clinical workflows, reducing manual effort, and increasing efficiency. By fostering the development of computer-aided solutions, this review supports the advancement of reliable and accessible IMAT/IMF quantification methods that have the potential to transform musculoskeletal imaging and patient care.

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.

Sports climbing has grown as a competitive sport over the last decades. This has leading to an increasing interest in guaranteeing the safety of the climber. In particular, operational errors, caused by the belayer, are one of the major issues leading to severe injuries. The objective of this study is to analyze and predict the severity of a pendulum fall based on the movement information from the belayer alone. Therefore, the impact force served as a reference. It was extracted using an Inertial Measurement Unit (IMU) on the climber. Additionally, another IMU was attached to the belayer, from which several hand-crafted features were explored. As this led to a high dimensional feature space, dimension reduction techniques were required to improve the performance. We were able to predict the impact force with a median error of about 4.96%. Pre-defined windows as well as the applied feature dimension reduction techniques allowed for a meaningful interpretation of the results. The belayer was able to reduce the impact force, which is acting onto the climber, by over 30%. So, a monitoring system in a training center could improve the skills of a belayer and hence alleviate the severity of the injuries.

Purpose To minimize the variability of surgical outcomes in strabismus surgery we evaluated machine learning models that predict the dosage for surgical correction of horizontal non-paretic strabismus, based on preoperative features and tried to determine feature importance with explainable artificial intelligence. Methods First, a structured retrospective analysis of patients who had strabismus surgery between 2003 and 2022 at the university clinic of Ulm was performed. A final streamlined dataset of 767 patients was included in the second step. We built a multilayer perceptron using the functional class from torch neural network to evaluate the data and build model 0. The features were analyzed with approaches from explainable artificial intelligence to check the attributions for plausibility. Results The highest number of predictions in the acceptable range was achieved with the Broyden-Fletcher-Goldfarb-Shanno algorithm. For model 0 the root mean square error for both labels were: L1 = 0.54 mm and L2 = 0.71 mm. For L1 and L2, 69% and 55% of the data was predicted within the acceptable range respectively. The preoperative angle of deviation as the most important feature for dosages was confirmed by feature permutation as well as integrated gradients, followed by near angle of deviation, use of simultaneous cover test, Hirschberg test angle and refraction of own glasses used. Conclusion The developed neural network seems to offer a way to predict dosage in strabismus surgery and could assist surgeons in their decision making. A significant improvement of prediction accuracy is expected after increasing the data basis.