Publikation

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.

Abstrakt

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.

Projekte

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence