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, Pages 42-55, Springer, Heidelberg, 2010.
Zusammenfassung
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.