Abstract. 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 biomechanical 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 activity 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.
Reiss et al. REF described a method for activity recognitions based on a biomechanical upper-body model using on-body sensors of IMUs.
Activity recognition using biomechanical model based pose estimation
EuroSSC
Computer Science