8th International Conference on Body Area Networks

Research Article

Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors

  • @INPROCEEDINGS{10.4108/icst.bodynets.2013.253613,
        author={Carolin Jakob and Patrick Kugler and Felix Hebenstreit and Samuel Reinfelder and Ulf Jensen and Dominik Schuldhaus and Matthias Lochmann and Bjoern Eskofier},
        title={Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors},
        proceedings={8th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2013},
        month={10},
        keywords={inertial sensors feedback training sports joint angles motion tracking extended kalman filter euler angles},
        doi={10.4108/icst.bodynets.2013.253613}
    }
    
  • Carolin Jakob
    Patrick Kugler
    Felix Hebenstreit
    Samuel Reinfelder
    Ulf Jensen
    Dominik Schuldhaus
    Matthias Lochmann
    Bjoern Eskofier
    Year: 2013
    Estimation of the Knee Flexion-Extension Angle During Dynamic Sport Motions Using Body-worn Inertial Sensors
    BODYNETS
    ACM
    DOI: 10.4108/icst.bodynets.2013.253613
Carolin Jakob1, Patrick Kugler1,*, Felix Hebenstreit1, Samuel Reinfelder1, Ulf Jensen1, Dominik Schuldhaus1, Matthias Lochmann2, Bjoern Eskofier1
  • 1: Digital Sports Group, Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany
  • 2: Institute of Sport Science and Sport, University of Erlangen-Nuremberg, Germany
*Contact email: patrick.kugler@cs.fau.de

Abstract

Motion analysis has become an important tool for athletes to improve their performance. However, most motion analysis systems are expensive and can only be used in a laboratory environment. Ambulatory motion analysis systems using inertial sensors would allow more flexible use, e.g. in a real training environment or even during competitions. This paper presents the calculation of the flexion-extension knee angle from segment acceleration and angular rates measured using body-worn inertial sensors. Using a functional calibration procedure, the sensors are first aligned without the need of an external camera system. An extended Kalman filter is used to estimate the relative orientations of thigh and shank, from which the knee angle is calculated. The algorithm was validated by comparing the computed knee angle to the output of a reference camera motion tracking system. In total seven subjects performed five dynamic motions: walking, jogging, running, jumps and squats. The averaged root mean squared error of the estimated knee angle was 8.2 degree (standard deviation 2.4 degree) over all motions, with an average Pearson-correlation of 0.971 (standard deviation 0.020). In the future this will allow the analysis of joint angles during dynamic sports movements.