Activity Recognition Using Biomechanical Model Based Pose Estimation

Attila Reiss, Gustaf Hendeby, Gabriele Bleser, Didier Stricker

In: P. Lukowitz , G. Kortuem , K. Kunze (Hrsg.). The 5th European Conference on Smart Sensing and Context. European Conference on Smart Sensing and Context (EuroSSC-2010) November 14-16 Passau Germany Seiten 42-55 Springer Heidelberg 2010.


In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biome- chanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented ac- tivity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence