@article{PrinzOrlowskiBecketal.2025, author = {Prinz, Alexander and Orlowski, Katja and Beck, Eberhard and Witte, Kerstin}, title = {Serious Games for Seniors With Dementia: A Pilot Study}, series = {Journal of Applied Gerontology}, volume = {2025}, journal = {Journal of Applied Gerontology}, publisher = {SAGE}, doi = {10.1177/07334648251350846}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:522-opus4-33440}, pages = {1 -- 11}, year = {2025}, abstract = {Dementia, marked by cognitive decline, significantly impacts daily life. With global prevalence rising, traditional treatments manage symptoms but have side effects and offer no cure. Non-pharmacological interventions, like serious games, are gaining importance. This study assesses the feasibility and benefits of serious games for people with mild to moderate dementia over a 10-week intervention. Sixty-one patients were recruited, with 35 completing the study. The intervention included six games focusing on physical and cognitive training. Outcome measures were motor function, cognitive assessments, quality of life, and depression. Results showed significant improvements in dynamic balance (p = .013) but no significant changes in other measures. The findings suggest that serious games are feasible and can improve motor functions like balance. However, short intervention periods may limit their impact on cognitive function and quality of life. Longer interventions and personalized game designs are recommended for greater benefits.}, language = {en} } @inproceedings{PepeBuchholzLasenby2024, author = {Pepe, Alberto and Buchholz, Sven and Lasenby, Joan}, title = {Clifford Group Equivariant Neural Network Layers for Protein Structure Prediction}, series = {Northern Lights Deep Learning Conference, VOL 233, 09-11 Januar 2024, Tromso, Norwegen}, booktitle = {Northern Lights Deep Learning Conference, VOL 233, 09-11 Januar 2024, Tromso, Norwegen}, publisher = {PMLR}, pages = {1 -- 7}, year = {2024}, abstract = {We employ Clifford Group Equivariant Neural Network (CGENN) layers to predict protein coordinates in a Protein Structure Prediction (PSP) pipeline. PSP is the estimation of the 3D structure of a protein, generally through deep learning architectures. Information about the geometry of the protein chain has been proven to be crucial for accurate predictions of 3D structures. However, this information is usually flattened as machine learning features that are not representative of the geometric nature of the problem. Leveraging recent advances in geometric deep learning, we redesign the 3D projector part of a PSP architecture with the addition of CGENN layers . CGENNs can achieve better generalization and robustness when dealing with data that show rotational or translational invariance such as protein coordinates, which are independent of the chosen reference frame. CGENNs inputs, outputs, weights and biases are objects in the Geometric Algebra of 3D Euclidean space, i.e. G3,0,0, and hence are interpretable from a geometrical perspective. We test 6 approaches to PSP and show that CGENN layers increase the accuracy in term of GDT scores by up to 2.1\\%, with fewer trainable parameters compared to linear layers and give a clear geometric interpretation of their outputs.}, language = {en} } @inproceedings{PepeLasenbyBuchholz2024, author = {Pepe, Alberto and Lasenby, Joan and Buchholz, Sven}, title = {CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Geometry-aware Camera Pose Regression}, series = {Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, US, 2024}, booktitle = {Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, US, 2024}, publisher = {IEEE}, doi = {10.1109/WACV57701.2024.00646}, pages = {6593 -- 6603}, year = {2024}, abstract = {We introduce CGAPoseNet+ GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet solves the issue of balancing rotation and translation components in the loss function, and can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G_ 4, 0. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+ GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41\% and the average translation error by 8.8\% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6\% and 19.9\%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+ GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression.}, language = {en} } @unpublished{PepeLasenbyBuchholz2024, author = {Pepe, Alberto and Lasenby, Joan and Buchholz, Sven}, title = {STAResNet: A Network in Spacetime Algebra to solve Maxwell's PDEs}, series = {arXiv}, journal = {arXiv}, publisher = {arXiv}, pages = {1 -- 17}, year = {2024}, abstract = {We introduce STAResNet, a ResNet architecture in Spacetime Algebra (STA) to solve Maxwell's partial differential equations (PDEs). Recently, networks in Geometric Algebra (GA) have been demonstrated to be an asset for truly geometric machine learning. In [1], GA networks have been employed for the first time to solve partial differential equations (PDEs), demonstrating an increased accuracy over real-valued networks. In this work we solve Maxwell's PDEs both in GA and STA employing the same ResNet architecture and dataset, to discuss the impact that the choice of the right algebra has on the accuracy of GA networks. Our study on STAResNet shows how the correct geometric embedding in Clifford Networks gives a mean square error (MSE), between ground truth and estimated fields, up to 2.6 times lower than than obtained with a standard Clifford ResNet with 6 times fewer trainable parameters. STAREsNet demonstrates consistently lower MSE and higher correlation regardless of scenario. The scenarios tested are: sampling period of the dataset; presence of obstacles with either seen or unseen configurations; the number of channels in the ResNet architecture; the number of rollout steps; whether the field is in 2D or 3D space. This demonstrates how choosing the right algebra in Clifford networks is a crucial factor for more compact, accurate, descriptive and better generalising pipelines.}, language = {en} } @inproceedings{PepeLasenbyBuchholz2024, author = {Pepe, Alberto and Lasenby, Joan and Buchholz, Sven}, title = {GA-ReLU: an activation function for Geometric Algebra Networks applied to 2D Navier-Stokes PDEs}, series = {ICLR 2024 Workshop on AI4DifferentialEquations In Science, Wien, {\"O}sterreich}, booktitle = {ICLR 2024 Workshop on AI4DifferentialEquations In Science, Wien, {\"O}sterreich}, pages = {1 -- 6}, year = {2024}, abstract = {Many differential equations describing physical phenomena are intrinsically geometric in nature. It has been demonstrated how this geometric structure of data can be captured effectively through networks sitting in Geometric Algebra (GA) that work with multivectors, making them suitable candidates to solve differential equations. GA networks however, are still mostly uncharted territory. In this paper we focus on non-linearities, since applying them to multivectors is not a trivial task: they are generally applied in a point-wise fashion over each real-valued component of a multivector. This approach discards interactions between different elements of the multivector input and compromises the geometric nature of GA networks. To bridge this gap, we propose GA-ReLU, a GA approach to the rectified linear unit (ReLU), and show how it can improve the solution of Navier-Stokes PDEs.}, language = {en} } @inproceedings{TranconyWidemannLepper2024, author = {Tranc{\´o}n y Widemann, Baltasar and Lepper, Markus}, title = {Imagine There's No Source Code: Replay Diagnostic Location Information in Dynamic EDSL Meta-programming}, series = {MPLR 2024: Proceedings of the 21st ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes}, booktitle = {MPLR 2024: Proceedings of the 21st ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes}, editor = {Ertl, M. Anton and Kirsch, Christoph M.}, publisher = {ACM}, address = {New York}, doi = {10.1145/3679007.3685061}, pages = {1 -- 11}, year = {2024}, abstract = {Programs in embedded domain-specific languages are realized as graphs of objects of the host language rather than as static input texts. This property enables dynamic meta-programming, but also makes it harder to attach location information to diagnostic messages that arise at a later stage, after the program graph construction. Thus, EDSL-generating expressions and algorithms can be difficult to debug. Here, we present a technique for transparently capturing and replaying location information about the origin of EDSL program objects. It has been implemented in the context of the LLJava-live EDSL-to-bytecode compiler framework on the JVM. The basic idea can be generalized to other contexts, and to any managed runtime environment with reified stack traces}, language = {en} } @inproceedings{DoudkinKindsmueller2024, author = {Doudkin, Alexander and Kindsm{\"u}ller, Martin Christof}, title = {Do not touch! - An artistic climate data visualization using motion capturing and 3D computer graphics}, series = {Mensch und Computer 2024 - Workshopband, Gesellschaft f{\"u}r Informatik e.V., 01.-04. September 2024, Karlsruhe, German}, booktitle = {Mensch und Computer 2024 - Workshopband, Gesellschaft f{\"u}r Informatik e.V., 01.-04. September 2024, Karlsruhe, German}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Karlsruhe}, doi = {10.18420/muc2024-mci-demo-319}, pages = {1 -- 3}, year = {2024}, abstract = {This demo explores an innovative artistic installation that creatively visualizes global temperature data using graphical visualization and motion capture technologies. By combining video-based posture capturing of nearby individuals with a dynamically rendered 3D model of the planet Earth, this installation offers an interactive and immersive experience. The goal is to transform climate change data into an engaging visual format, making it more accessible and impactful for a wide range of audiences.}, language = {en} } @article{VaherTammSalusetal.2023, author = {Vaher, Ivi and Tamm, Anna-Liisa and Salus, Marit and Reisberg, Kirkke and V{\"a}hi, Aleksandra and Pallon, Helena and Paeste, Andra and Pirk, B{\"a}thel-Betty and Merila, Margus and Schrader, Thomas}, title = {Effectiveness of massage chair and classic massage in recovery from physical exertion: a pilot study}, series = {Journal of Novel Physiotherapy and Rehabilitation}, volume = {2023}, journal = {Journal of Novel Physiotherapy and Rehabilitation}, number = {7}, publisher = {HSPI}, doi = {10.29328/journal.jnpr.1001050}, pages = {8 -- 15}, year = {2023}, abstract = {Quick and cost-effective recovery is foundational to high-quality training and good competition results in today's sports. The aim of the research was to elucidate the effects of hand and massage chair massage on the biomechanical parameters of muscles of lower limbs and back, indicators of Pain Pressure Thresholds (PPT) and subjectively perceived fatigue. A total of 32 female recreational athletes (18 - 50 years old) were assigned to a hand massage, massage chair, or lying down the group. They were measured for muscle biomechanical properties (MyotonPro), PPT (Wagner Instruments) and subjectively perceived fatigue (VAS scale) before and after fatigue tests and treatment. The recovery procedure and subjective satisfaction with treatment were rated on a Likert scale. Changes in the median value of m. rectus femoris and m. gastrocnemius stiffness with treatment showed that hand massage could be more effective in reducing stiffness, as compared to chair massage. Hand massage may have benefits for recovery from physical exertion, but due to the individuality of subjects, detailed methodological studies are needed to evaluate the effects of massage chair vs. hand massage.}, language = {en} } @inproceedings{TetzlaffPurohitSpalleketal.2023, author = {Tetzlaff, Laura and Purohit, Anne-Maria and Spallek, Jacob and Holmberg, Christine and Schrader, Thomas}, title = {Evaluating Interoperability in German Critical Incident Reporting Systems}, series = {German Medical Data Sciences 2023 - Science. Close to People. Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, Heilbronn, Germany}, booktitle = {German Medical Data Sciences 2023 - Science. Close to People. Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, Heilbronn, Germany}, publisher = {IOS Press}, doi = {10.3233/SHTI230722}, pages = {249 -- 257}, year = {2023}, abstract = {In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness.}, language = {en} } @article{WeberSteinPilgermannetal.2023, author = {Weber, Simon B. and Stein, Stefan and Pilgermann, Michael and Schrader, Thomas}, title = {Attack Detection for Medical Cyber-Physical Systems - A Systematic Literature Review}, series = {IEEE Access}, journal = {IEEE Access}, number = {11}, publisher = {IEEE}, doi = {10.1109/ACCESS.2023.3270225}, pages = {41796 -- 41815}, year = {2023}, abstract = {The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain's unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.}, language = {en} }