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  • Activity Recognition and Event Detection in Table Tennis
    (Own Funds)
    Digitalization of sports is also taking place in table tennis. This is caused by body-worn Wearables. This project captures, analyzes and processes the motion and movement of players,  ball characteristics and other interesting parameters during table tennis games and exercises. In contrast to common standard camera-based analysis within predefined laboratory environments used for most ball sports, only sensors mounted on the racket are included. All necessary electronics should ideally be hidden inside the racket, that the user does not feel affected. For motion analysis, mainly inertial sensors (accelerometers and gyroscopes) are used, as well as magnetometers for the absolute alignment in space and specific piezoelectric sensors for vibration detection. First, the acceleration, the angular velocity and absolute orientation of the racket are measured to classify the stroke type using pattern recognition algorithms.  It is possible to differentiate between forehand and backhand stroke types and various spin types. In addition, the ball impact event is verified by the vibration sensors. Afterwards, the resulting ball speed and spin are estimated shortly after this impact. Finally, the point of impact on the racket is localized by triangulation methods, similar to epicenter localization during earthquakes. All data is calculated on the embedded microcontroller and transferred to a mobile device, such as an Android smartphone via Bluetooth. There, the data is provided to the player as feedback for training support or statistics.
  • DailyHeart
    (Own Funds)
    Term: Aug 15, 2014 - Aug 15, 2017
    DailyHeart is a system for mobile ECG analysis that addresses the whole pipeline from data acquisition over data processing to data visualization. It features a novel low-power data acquisition ECG hardware using Bluetooth Low Energy, and an application for Android-based mobile devices that offers several modes for cardiac feedback, from measuring the current heart rate to continuously monitoring the user’s ECG throughout the day. Furthermore, it incorporates methods for human computer interaction concepts (e.g. smartwatches or Google Glass) to provide cardiac feedback to the user throughout the day, and depending on the current context.

    One clinical use case of DailyHeart is the unobtrusive monitoring of the users’ heart rate variability (HRV) by combining it with information on posture changes, e.g. changes from lying down to sitting or standing up. Initial results have shown that multiple measurements throughout the day can already indicate a possible disorder of the autonomous nervous system (e.g. Parkinson's Disease), and thus provide a recommendation of consulting a medical expert.

  • Data Mining in the U.S. National Toxicology Program (NTP) Database
    (Own Funds)
    Term: Mar 1, 2015 - May 31, 2015
  • Digital Sports Bavaria: Implementation and validation of innovative Cyber Physical Systems and Human Computer Interaction concepts for future Wearable Computing trends in sport and fitness
    (Third Party Funds Single)
    Term: Apr 1, 2015 - Mar 31, 2018
    Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
    Digitalization becomes more and more part of different areas in daily life, e.g. sports and fitness: Wearables, nowadays especially step counters or activity trackers, support humans by individual promotion of health and improvement of fitness. Due to the various application areas, these Wearables are a profitable market for the future, but current existing systems have the following disadvantages:

    Currently available sensors on the market are external devices which are manually put into apparel. An incorrect use, based on wrong placement of the system, leads e.g. to movement patterns of steps which deviate from a usual step. This deviation is a challenge for further processing.

    Signal analysis is mainly based on the classification of movements, e.g. the detection of steps. In many applications, the quality of movement is important. Based on the information about, how a step is performed, an assessment of e.g. the state of health of a person can be performed.

    The user interfaces are mainly based on simple text and/or graphical output on mobile devices or PC, e.g. number of steps per day and during one week. Nevertheless, for long-term success of systems, innovative approaches are needed.

    Wearables mainly communicate with mobile devices, e.g. smartphones or a PC. Data are transmitted to a server via web browser for storage and display. This kind of system architecture is called wireless sensor network. In many applications, this simple system architecture is not sufficient. Furthermore, the user pays a fixed amount of money for the complete system, independent of the usage of e.g. server and services. For more complex algorithms and a location-independent application of the system, other system architectures have to be used. Computationally intensive algorithms for step segmentation, which e.g. are combined with a comparison of steps and statistics from a database, should run directly on a remote server. Furthermore, it is proposed that the cost of the system should be dependent on the usage of server and services.

    The goal of the project is to develop a generic platform for future Wearable Computing trends in sports and fitness, which has the following four features: (i) permanent availability by integrated sensors in apparel using e-textiles (sensor integration), (ii) intelligent and flexibly adaptable sensor data processing for a detailed analysis of complex movement patterns, which provides more than just a classification (signal analysis), (iii) development of innovative user interfaces (Human Computer Interaction), and (iv) extension of the system architecture of wireless sensor networks with a combination of cloud computing methods (Cyber Physical Systems).

  • ESI@Fitness
    (Third Party Funds Group – Sub project)
    Overall project: ESI-Anwendungszentrum für die digitale Automatisierung, den digitalen Sport und die Automobilsensorik der Zukunft
    Term: Jan 1, 2015 - Dec 31, 2018
    Funding source: Bayerische Staatsministerien
    URL: http://www.esi.fau.de/
  • Fall risk detection for Parkinson's disease via intelligent gait analysis
    (Third Party Funds Single)
    Term: Jan 1, 2018 - Dec 31, 2019
    Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
    The ability to walk defines human nature and limits the mobility, independence, and quality of life. Falls are the leading cause of both fatal and nonfatal injuries among older adults, causing severe injuries such as hip fractures, head trauma, and death. Increased fall risk is a key symptom in Parkinson’s disease (PD), limiting the independency and mobility of patients.

    So far, no validated technical solutions exist to identify the individual’s rising fall risk before the first fall occurs. Therefore, we will investigate algorithms, that are able to predict the fall risk based on specific gait patterns, captured by shoe integrated inertial sensors. The data for the evaluation of fall risk associated gait patterns will be acquired by means of a continuous long-term monitoring system.

    To ensure a successful progress of this project we will combine three strategies in the research and development phase:

    1. Usage of distinct sensors that enable gait assessment with high biomechanical resolution
    2. Development and evaluation of machine learning based gait pattern algorithms
    3. Digital biobanking of clinical distinct gait patterns to individualize fall risk monitoring.

    The overall goal of the project is the investigation of novel machine learning based algorithms that enable the determination of PD patients’ fall risk using continuous gait data. Since existing algorithms and test procedures in related clinical research are typically limited to one-time assessments, we will investigate new algorithms for a continuous gait monitoring system, that will identify disease specific changes of gait with a high reliability.

    At the same time, we will generate clinical understanding and validated data for individualized applicability that is required for medical product licensing, as well as economic effect sizes of technology application in healthcare strategies.

  • Green Belt ML@Operations - Machine Learning for Specific Use Cases in Production and Quality
    (Third Party Funds Single)
    Term: Nov 1, 2017 - Oct 31, 2019
    Funding source: Bundesministerium für Bildung und Forschung (BMBF)
    Die Digitalisierung birgt große Potenziale zur Steigerung der Ressourceneffizienz industrieller Produktionsprozesse. Durch Technologien im Kontext von Industrie 4.0 können produktionsnahe Daten kurzzyklisch erfasst und aggregiert werden. In Anbetracht der dadurch zunehmenden Datenkomplexität und des Datenvolumens stehen Mitarbeiter jedoch vor der Herausforderung, diese Daten zu analysieren und zu interpretieren sowie die Nachhaltigkeit der eingeleiteten Maßnahmen zu bewerten, wobei die kognitiven Fähigkeiten oft an ihre Grenzen stoßen.

    Verfahren des Maschinellen Lernens (ML) können hier neue Formen der Arbeitsteilung zwischen Maschinen bzw. Software als Entscheidungsvorbereiter und Mitarbeitern als Problemlöser zu ermöglichen. In der industriellen Praxis werden ML-Verfahren meist situativ und von Experten entwickelt eingesetzt, so dass der Aufwand entsprechend hoch ist. Des Weiteren verfügen kleine und mittlere Unternehmen (kmU) häufig nur über wenig Ressourcen und Expertise, um diese Potenziale zu nutzen.

    Ziel dieses Projektes ist es, ein Qualifizierungskonzept zu entwickeln und durchzuführen, um den Kenntnisstand bzgl. ML-Verfahren von Mitarbeitern in Produktions- und Qualitätsbereich sowie von Studierenden mit den genannten Schwerpunkten gezielt zu erweitern. Die Teilnehmer entscheiden sich dabei entweder für die Spezialisierungsrichtung "Produktion" oder "Qualität". Jede Spezialisierungsrichtung besteht aus vier praxisorientierten Anwendungsfällen, in denen die Teilnehmer geeignete ML-Verfahren kennenlernen und in konkreten individuellen Projekten mit ca. 10 Wochen Dauer anwenden. Die Anwendungsphase wird von der wissenschaftlichen Leitung des Projekts individuell gecoacht. Die Anwendungsfälle orientieren sich an bestehenden Geschäftsprozessen und Problemstellungen in der Industrie zum Qualitätsmanagement und zur Optimierung von Produktionsprozessen, wodurch ein einfacher Transfer und eine hohe Akzeptanz auf industrieller Seite sichergestellt werden soll. 

  • Information management system for automated quality assessment in radiotherapy
    (Third Party Funds Single)
    Term: Aug 15, 2016 - Aug 14, 2018
    Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
    The increasing complexity of radiotherapy poses a serious challenge to
    quality management processes. Proper functioning of devices, precise
    control of process variables and thus ultimately the safety of the
    patient regarding exposure to radiation must be ensured. However, The
    complexity of the radiotherapy workflow lead to seriouos accidents in
    the past. Up to date, the quality of only partial radiotherapy workflow
    steps is properly assessed and the workflow as a whole is not assessed.
    The goal of this project is to develop an integrated an automatic
    quality management information system for a proactive error prevention.
    Based on quality measures, the whole workflow will be monitored. Data
    Mining, Benchmarking and machine learning tools will be used to detect
    potential faults in advance.
  • Innovation Lab for Wearable and Ubiquitous Computing
    (Third Party Funds Single)
    Term: Mar 1, 2017 - Feb 28, 2019
    Funding source: Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst (ab 10/2013)
    URL: https://www.mad.tf.fau.de/research/projects/innovation-lab-for-wearable-and-ubiquitous-computing/
    The Innovation Lab for Wearable and Ubiquitous Computing is a project funded by the Center for Digitalization Bavaria. The goal of this project is the implementation of a practical course, where students develop innovative prototypes in the fields of Wearable and Ubiquitous Computing by applying agile development techniques. The project ideas originate from three sources: the students themselves, researcher or external industry partners.
  • miLife - an innovative wearable computing platform for data analysis of wearable sensors to be used in team sports and health
    (Third Party Funds Single)
    Term: Aug 1, 2011 - Oct 31, 2014
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie (StMWIVT) (bis 09/2013)
    Body Sensor Networks are getting more and more important in sports and health. Currently, various isolated applications exist that use body sensors to assist athletes and monitor elderly people. Systems like the adidas miCoach and Nike+ prove the potential of information and communication engineering technology for manufacturers of sports equipment. The perfect product for a leading position in this market would be a central, flexible and generic wearable computing platform instead of isolated applications. To facilitate such a solution, sensors integration in clothing and sports equipment and data analysis capabilities have to be substantially advanced. Additionally, to succeed on the market, new communication and sensor technologies as well as innovative applications have to be developed.

    The goal of the project is to bundle and enhance the expertise of the project partners in the described field to develop innovative products. The existing miCoach platform will be the basis for a comprehensive communication and application platform for body sensor network data called "miLife". This platform will provide flexible sensor connection, data analysis and social networking capabilities for applications in team sports, exercise motivation and health monitoring.

  • Non-invasive determination of the human hydration level
    (Third Party Funds Single)
    Term: Jan 1, 2013 - Dec 31, 2017
    Funding source: Stiftungen
    The aim of the research project is the development of an embedded system for the determination of the human hydration level with non-invasive sensors. The combination of non-invasive sensors and embedded systems enables the determination of the hydration level in many new situations. For example, the hydration level of athletes could be monitored to maintain their optimal performance or the system could be connected to clinical warning systems in hospitals to monitor the hydration level of patients.
  • Performance Analysis in Team Sports
    (Third Party Funds Single)
    Term: Jun 1, 2017 - Jun 1, 2020
    Funding source: Industrie
    Performance Analysis in team sports is an emerging field in computer science. In Europe's leagues, a large amount of data is recorded during the season. Based on methods of machine learning and signal processing an automated, fast and accurate analysis of soccer matches is possible.

    In this project, the performance of a single player and the behavior of the whole team (e.g. tactics) is calculated based on position and inertial sensor data.

  • Spieldatenbasierte Leistungsindikatoren im Profifußball
    (Third Party Funds Single)
    Term: Jun 8, 2015 - Feb 22, 2016
    Funding source: Industrie
  • RoboCup Robot Soccer
    (Own Funds)
    Term: Jan 1, 2008 - Jan 1, 2011
    RoboCup is a international initiative to promote research in artificial intelligence and autonomous mobile robots. Each year the RoboCup Foundation hosts international tournaments where top research groups of Universities from the whole world participate. Since 2008, the University of Erlangen-Nuremberg also has its own RoboCup team that participates in the small-size league. This league is one of the smallest and fastest RoboCup leagues. Five wheeled robots per team are playing on a field of about 6m x 4m. The maximum size for each robot is 18cm in diameter and a height of 15 cm. The robots get information about the current game situation from two cameras above the field and an external computer, which communicates with the robots via a wireless link. In Erlangen the team is organized as an interdisciplinary student project at the Technical Department. The main goals of this project are to foster creative ideas and team work among technical students from electrical engineering, mechatronics and computer science. Research topics include topics from pattern recognition, embedded systems and artificial intelligence. In the scope of this project the Pattern Recognition Lab employs statistical estimation techniques and tries to extend them towards automotive applications. To promote the project, a non-profit organisation called "Robotics Erlangen e.V." was founded in 2008. In this organization team members as well as supporters of the group are brought together. The project is funded in part by tuition fees as well as private and industry donations.
  • Theoretical Machine Learning
    (Own Funds)
    Term: Jan 1, 2011 - Jan 1, 2014
    This project summarizes theoretical contributions to machine learning research.
  • Wearables in Sports
    (Own Funds)
    Term: Jun 1, 2017 - Jun 1, 2020
    In this project wearables for sports or rehabilitation are developed. They can be used for training control or analysis.
  • Capital4Health
    (Third Party Funds Group – Overall project)
    Term: Feb 1, 2015 - Jan 31, 2018
    Funding source: BMBF / Verbundprojekt
    URL: http://www.capital4health.de