miLife - an innovative wearable computing platform for data analysis of wearable sensors to be used in team sports and health

Third party funded individual grant

Project Details

Project leader:
Prof. Dr. Björn Eskofier

Project members:
Ulf Jensen
Dominik Schuldhaus
Prof. Dr. med. Johannes Kornhuber
Prof. Dr.-Ing. Joachim Hornegger
Heike Leutheuser

Contributing FAU Organisations:
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Lehrstuhl für Informatik 5 (Mustererkennung)
Lehrstuhl für Psychiatrie und Psychotherapie
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)

Funding source: Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie (StMWIVT) (bis 09/2013)
Start date: 01/08/2011
End date: 31/10/2014

Abstract (technical / expert description):

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.

External Partners

Adidas AG
Astrum IT GmbH


Schuldhaus, D., Leutheuser, H., & Eskofier, B. (2014). Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy. In 9th International Conference on Body Area Networks (pp. 97-103). London, GB.
Schuldhaus, D., Dorn, S., Leutheuser, H., Tallner, A., Klucken, J., & Eskofier, B. (2013). An Adaptable Inertial Sensor Fusion-Based Approach for Energy Expenditure Estimation. In Goh James (Eds.), The 15th International Conference on Biomedical Engineering (pp. 124-127). University Town, Singapore, SG: Heidelberg: Springer.
Schuldhaus, D., Leutheuser, H., & Eskofier, B. (2013). Classification of Daily Life Activities by Decision Level Fusion of Inertial Sensor Data. In ACM Digital Library (Eds.), 8th International Conference on Body Area Networks (pp. 77-82). Boston, US.
Jakob, C., Kugler, P., Hebenstreit, F., Reinfelder, S., Jensen, U., Schuldhaus, D.,... Eskofier, B. (2013). Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors. In IEEE (Eds.), BodyNets 2013 (pp. n/a). Boston, MA, USA, US.
Leutheuser, H., Schuldhaus, D., & Eskofier, B. (2013). Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset. Plos One, 8.0(10.0), e75196.

Last updated on 2018-22-11 at 18:01