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OFFIS-Institute for Information Technology, Germany

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28 Papers

Enhancing Fracture Aftercare Through a Human-Centered Mobile App Design

Topics: Mobile Technologies for Healthcare Applications; Design and Development of Methodologies for Healthcare IT; eHealth Applications; Human-Machine Interaction in Healthcare IT; Usability and UX of Healthcare IT

Authors: Matthias Maszuhn , Felix Jansen , Frerk Aschwege and Andreas Hein

Affiliation: OFFIS e. V., Escherweg 2, Oldenburg, Germany

Digital Object Identifier: 10.5220/0013307500003911

Proceedings of HEALTHINF 2025, ISBN: 978-989-758-731-3

Quick Abstract

The rehabilitation process after fractures is crucial for achieving full recovery and maintaining patients’ quality of life, yet it faces growing obstacles due to demographic changes and healthcare resource shortages. This paper proposes a mobile app prototype for physiotherapy aftercare, integrating features like exercise assistance, load monitoring, and collaborative documentation to enhance patient support and accessibility. Employing a Human-Centered Design approach, requirements were gathered initially through a comprehensive literature review, followed by the creation of user personas. Final requirements were then refined through semi-structured interviews with physiotherapists and clinical staff. The prototype was subsequently evaluated in a user study with 16 participants using the Think-Aloud method and the User Experience Questionnaire (UEQ). Results indicated high user satisfaction with features such as education, exercise guidance, and progress tracking, though minor usability improvements were identified. By providing real-time feedback, clear progress tracking, and personalized guidance, the app aims to improve patient compliance with rehabilitation protocols, ensuring more consistent engagement throughout the recovery process. Future iterations will focus on expanding functionality and validating the solution across diverse demographics, emphasizing its potential to significantly improve rehabilitation outcomes.

Predicting Falls from Operational Data: Insights and Limitations of Using a Non-Specialized Database

Topics: Evaluation and Use of Healthcare IT; Pattern Recognition and Machine Learning; Big Data in Healthcare; Data Mining and Data Analysis; Integration and Scaling-up of Healthcare IT in Real Care Settings

Authors: Julien Räker ¹ , Patrick Elfert ¹ , Cletus Brauer ² , Marco Eichelberg ¹ , Frerk Müller-von Aschwege ¹ and Andreas Hein ¹

Affiliations: ¹ R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany , ² Johanniter-Unfall-Hilfe e.V., Oldenburg, Germany

Digital Object Identifier: 10.5220/0013298600003911

Proceedings of HEALTHINF 2025, ISBN: 978-989-758-731-3

Quick Abstract

Falls among the elderly are a significant public health concern. This study investigates the feasibility of predicting falls using an operational dataset from Johanniter-Unfall-Hilfe (JUH) home emergency call system, which was not created under laboratory conditions for scientific purposes. An anonymized dataset containing records from 160,281 participants in Germany was analyzed. Statistical analysis identified 104 out of 400 features significantly associated with falls, though with weak correlations (Cramer’s V ranging from 0.006 to 0.071). A one-class Support Vector Machine (SVM) was employed due to the absence of explicit non-fall cases, achieving a true positive rate of 55.10%. The lack of explicit non-fall data prevented evaluation of specificity and overall accuracy. The study demonstrates the potential of using operational datasets for fall prediction but highlights significant limitations due to data quality issues, such as the lack of explicit fall records, absence of non-fall cases, lack of temporal data, and missing values. Recommendations are made to improve data collection practices to enhance the utility of such datasets for predictive modeling.

Privacy-Preserving Self-Organization in Distributed Energy Scheduling

Topics: Multi-Agent Systems; Distributed Problem Solving; Self Organizing Systems; Group Decision Making (with cooperation, coordination, negotiation, interaction protocols); Privacy, Safety, Security, and Ethical Issues

Authors: Joerg Bremer ¹ and Sebastian Lehnhoff ²

Affiliations: ¹ Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg, Germany , ² R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg, Germany

Digital Object Identifier: 10.5220/0013094900003890

Proceedings of ICAART 2025, ISBN: 978-989-758-737-5

Quick Abstract

Negotiation among agents that are controlling and orchestrating a set of distributed processes often relies on frequent data exchange to allow solution evaluation and thus convergence towards a joint solution. Solving decentralized coordination problems with coalitions of agents that exchange messages and information to build beliefs for problem solving, inevitably allows insight into other agents’ operational options. Keeping local information private is thus of utmost importance for a wide user acceptance of such algorithms. We present an extension to a distributed, self-organizing algorithm for energy scheduling in virtual power plants or energetic neighborhoods that keeps all information about possible operations of participating energy resources private. For calculations during optimization the algorithm relies on secret sharing and joint multi-party computations. We evaluate the algorithm against the original non privacy-preserving standard version and present some insights for future work.

Sampling Strategies for Static Powergrid Models

Topics: Performance Analysis; Domain-Specific Tools; Data Analytics and Simulation; Energy Systems

Authors: Stephan Balduin , Eric Msp Veith and Sebastian Lehnhoff

Affiliation: OFFIS - Institute for Information Technology, Escherweg 2, Oldenburg, Germany

Digital Object Identifier: 10.5220/0011306400003274

Proceedings of SIMULTECH 2022, ISBN: 978-989-758-578-4

Quick Abstract

Machine learning and computational intelligence technologies gain more and more popularity as possible solution for issues related to the power grid. One of these issues, the power flow calculation, is an iterative method to compute the voltage magnitudes of the power grid’s buses from power values. Machine learning and, especially, artificial neural networks were successfully used as surrogates for the power flow calculation. Artificial neural networks highly rely on the quality and size of the training data, but this aspect of the process is apparently often neglected in the works we found. However, since the availability of high quality historical data for power grids is limited, we propose the Correlation Sampling algorithm. We show that this approach is able to cover a larger area of the sampling space compared to different random sampling algorithms from the literature and a copula-based approach, while at the same time inter-dependencies of the inputs are taken into account, which, from the other algorithms, only the copula-based approach does.

Structured Planning of Hardware and Software Co-simulation Testing of Smart Grids

Topics: Domain-Specific Tools; Hardware in the Loop Simulation; Clean Energy and Power Systems; Co-Simulation; Simulation of Energy Systems

Authors: Rami Elshinawy , Rebeca P. Ramírez Acosta , Jan Sören Schwarz and Sebastian Lehnhoff

Affiliation: OFFIS Institute, Escherweg 2, Oldenburg, Germany

Digital Object Identifier: 10.5220/0009820701970208

Proceedings of SIMULTECH 2020, ISBN: 978-989-758-444-2

Quick Abstract

The traditional electricity grid is ought to become a smart grid. One reason is the integration of volatile renewable energy generation, which poses the demand for advanced communication and control technology. This results in a change of the overall system dynamics, which in turn require testing to be done in early stages and with a more holistic approach. However, there is a gap in the transition from system specification and static analysis to an experimental setup validating these specification. This paper demonstrates first steps towards a structured methodology for deriving validation experiments for smart grids from initial system requirements. Our approach aims to fill the introduced gap by integrating Smart Grid Architecture Model (SGAM) for static analysis, Holistic Testing Description (HTD) for experimental analysis, and additional tools in a new workflow. Initial assessment of the workflow has been validated on its ability to select the most suitable components and test beds to perform an experiment. Therefore, two case studies were selected, one in the electricity power domain and another in the electricity market domain.

A Clustering-based Approach to Determine a Standardized Statistic for Daily Activities of Elderly Living Alone

Topics: Home Monitoring; Physiological and Behavioral Modeling

Authors: Alexander Gerka ¹ , Christian Lins ¹ , Max Pfingsthorn ¹ , Marco Eichelberg ¹ , Sebastian Müller ² , Christian Stolle ² and Andreas Hein ²

Affiliations: ¹ OFFIS - Institute for Information Technology, Escherweg 2, Oldenburg and Germany , ² Department for Health Services Research, Carl-von-Ossietzky University, Oldenburg and Germany

Digital Object Identifier: 10.5220/0007369302640271

Proceedings of HEALTHINF 2019/BIOSTEC, ISBN: 978-989-758-353-7

Quick Abstract

The modeling of behavior by monitoring activities of daily living allows caregivers to recognize early stages of dementia. Therefore, many monitoring systems were presented in recent years. In this work, we present a behavior modeling system that is based only on two adjustable parameters and provides a single standardized output statistic. Therefore, this system enhances the comparison of recent and future activity monitoring systems. The approach is comprised of three parts: First, the clustering of power plug data to detect time windows in which appliances are used regularly. Second, the calculation of a comparison Matrix. Third the test of change using the χ2-statistic. We tested this approach successfully in a seven-month field study with two healthy subjects. We showed that the χ2-statistic reflected how regular activities were performed and that one to two months, depending on the regularity of the performed activities, provide the necessary amount of reference data for our approach to work.

Related Ontology Subjects/Areas/Topics (3)

Applications and Uses

Home Monitoring and Assisted Living Applications

Sensor Networks

Lazy Agents for Large Scale Global Optimization

Topics: Soft Computing; Distributed Problem Solving; Swarm Intelligence; Self Organizing Systems

Authors: Joerg Bremer ¹ and Sebastian Lehnhoff ²

Affiliations: ¹ Department of Computing Science, University of Oldenburg, Uhlhornsweg, Oldenburg and Germany , ² R&D Division Energy, OFFIS – Institute for Information Technology, Escherweg, Oldenburg and Germany

Digital Object Identifier: 10.5220/0007571600720079

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

Optimization problems with rugged, multi-modal Fitness landscapes, non-linear problems, and derivative-free optimization entails challenges to heuristics especially in the high-dimensional case. High-dimensionality also tightens the problem of premature convergence and leads to an exponential increase in search space size. Parallelization for acceleration often involves domain specific knowledge for data domain partition or functional or algorithmic decomposition. We extend a fully decentralized agent-based approach for a global optimization algorithm based on coordinate descent and gossiping that has no specific decomposition needs and can thus be applied to arbitrary optimization problems. Originally, the agent method suffers from likely getting stuck in high-dimensional problems. We extend a laziness mechanism that lets the agents randomly postpone actions of local optimization and achieve a better avoidance of stagnation in local optima. The extension is tested against the original method as well as against established methods. The lazy agent approach turns out to be competitive and often superior in many cases.

Related Ontology Subjects/Areas/Topics (12)

Agents	Distributed Problem Solving	Intelligent Control Systems and Optimization
Artificial Intelligence	Enterprise Information Systems	Self Organizing Systems
Artificial Intelligence and Decision Support Systems	Evolutionary Computing	Soft Computing
Computational Intelligence	Informatics in Control, Automation and Robotics	Swarm Intelligence

A Cascading Chi-shapes based Decoder for Constraint-handling in Distributed Energy Management

Topics: Representation Techniques; Evolutionary Search and Meta-heuristics ; Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance

Authors: Joerg Bremer ¹ and Sebastian Lehnhoff ²

Digital Object Identifier: 10.5220/0006926101840191

Proceedings of IJCCI 2018/IJCCI, ISBN: 978-989-758-327-8

Quick Abstract

A steadily rising share of small, distributed, and volatile energy units like wind energy converters solar panels, co-generation plants, or similar assigns new tasks and challenges to the smart grid regarding operation and control. The growing complexity of the grid also imposes a growing complexity of constraints that restrict the validity of solutions for operation schedules, resource capacity utilization or grid compliance. Using surrogate models as an abstraction layer has recently become a promising approach for constructing algorithms independently from any knowledge about the actual device or operation restricting constraints. So called decoders as a special constraint handling technique allow for systematically generating feasible solutions directly from a learned surrogate model. Some decoder approaches based on support vector machines have already been implemented, but suffer from performance issues and a sensible parametrization. We propose a new type of decoder based on a cascade of χ-shapes to overcome these problems. The applicability is demonstrated with a simulation study using different types of flexible energy units.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Evolutionary Computing		Soft Computing
Computational Intelligence		Representation Techniques

Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation

Topics: Wearable Health Informatics; Development of Assistive Technology; Pattern Recognition and Machine Learning; Medical Informatics

Authors: Christian Lins ¹ , Sebastian M. Müller ² , Max Pfingsthorn ² , Marco Eichelberg ² , Alexander Gerka ² and Andreas Hein ¹

Affiliations: ¹ Carl von Ossietzky University, Germany , ² OFFIS - Institute for Information Technology, Germany

Digital Object Identifier: 10.5220/0006598904780485

Proceedings of HEALTHINF 2018/BIOSTEC, ISBN: 978-989-758-281-3

Quick Abstract

In this paper, we present an online method for the unsupervised segmentation of skeletal motion capture data for the assessment of unfavorable or harmful postures in the context of musculoskeletal disorders. The long-time motion capture data is segmented into short motion sequences using joint distances of the captured skeleton. We use the difference between joint distance matrices to detect variances in motion dynamics in which the motion is separated into either a dynamic motion or a static posture. Then, the static posture can be evaluated using well-known posture assessment methods such as the Ovako Working postures Analysing System (OWAS) to derive risk factors for musculoskeletal disorders. The algorithm works in real-time so that it can be incorporated in live warning systems for unfavorable or harmful postures. We evaluated the segmentation algorithm by comparing it with results from state-of-the-art offline motion segmentation algorithms as gold standard. Results show that the algorithm approaches the performance of state-of-the-art offline segmentation algorithms.

Related Ontology Subjects/Areas/Topics (9)

Biomedical Engineering	Devices	Pattern Recognition and Machine Learning
Biomedical Signal Processing	Health Information Systems	Physiological Computing Systems
Development of Assistive Technology	Human-Computer Interaction	Wearable Sensors and Systems

Abstract Information Model for Geriatric Patient Treatment - Actors and Relations in Daily Geriatric Care

Topics: Knowledge Management; Evaluation and Use of Healthcare IT; Medical Informatics; Design and Development of Methodologies for Healthcare IT

Authors: Lars Rölker-Denker and Andreas Hein

Affiliation: University of Oldenburg, Germany

Digital Object Identifier: 10.5220/0006106902220229

Proceedings of HEALTHINF 2017/BIOSTEC, ISBN: 978-989-758-213-4

Quick Abstract

The authors propose an abstract information for geriatric care, the geriatric information model (GIM). They adopt an information model from cancer care and introduce characteristics for geriatric care (patient population, multidisciplinary and multi-professional approach, cross-sectoral approach). Actors (patients, physicians, therapists, organisations), information objects, and information relations are defined. The GIM is validated by mapping four typical knowledge processes (multi-professional geriatric team session, interdisciplinary clinical case conferences, tumor boards, transition management) onto the model. The GIM is stated as useful for understanding information flows and relations in geriatric care. All processes for validation can be mapped onto GIM. In future work the GIM should be tested with more knowledge process and could also be used for identifying gaps in the IT support of geriatric care. A study on high and low information quality in geriatric care is also proposed.

Related Ontology Subjects/Areas/Topics (10)

Biomedical Engineering	Health Information Systems	Ontologies and the Semantic Web
Data Engineering	Information Systems Analysis and Specification	Society, e-Business and e-Government
Enterprise Information Systems	Knowledge Management	Web Information Systems and Technologies
Evaluation and Use of Healthcare IT

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