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Embedded Classification

Classification on Embedded Systems
  • Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true, for example, for the fields of sports, automotive, and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources.

    With the Embedded Classification Software Toolbox (ECST), we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and, at the same time, high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification accuracy, and finally analyzes the complexity of the trained system. 

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    Journal Articles
    Approaching the accuracy-cost conflict in embedded classification system design
    Pattern Analysis and Applications, vol. -, pp. 1-17, 2015 (BiBTeX, Who cited this?)
    Articles in Conference Proceedings
    Pattern Recognition (ICPR), 2012 21st International Conference on (21st International Conference on Pattern Recognition), Tsukuba, Japan, November 11-15, 2012, pp. 2266-2269, 2012, ISBN 978-4-9906441-1-6 (BiBTeX, Who cited this?)
Classification of Kinematic Swimming Data with Emphasis on Resource Consumption
  • The collection of kinematic data with a head-worn sensor is a promising approach for swimming data analysis in the context of athlete support systems. We present a new approach of analyzing these data and describe a system that segments the lanes of a swimming session and classifies the swimming style of each lane. Special emphasis was put on the algorithm efficiency and the analysis of the resource demands to be able to port the implementation to an embedded microcontroller. For developing the system, data of twelve subjects was collected. The data incorporated two different turn styles that mark the end of a lane as well as the four main swimming styles backstroke, breaststroke, butterfly and freestyle. All turns were successfully identified from the turn detection. Our fully automatic swimming style classification reached a classification rate of 95.0%. The results from the resource consumption analysis can be used to support the decision for the embedded target hardware of a head-worn swimming training system.

    Articles in Conference Proceedings
    Jensen, Ulf; Prade, Franziska; Eskofier, Björn
    Body Sensor Networks (BSN), 2013 IEEE International Conference on (IEEE International Conference on Body Sensor Networks (BSN)), Cambridge, USA, May 6 - 9, pp. -, 2013, ISBN 978-1-4799-0331-3 (BiBTeX, Who cited this?)