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.
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Reiss, A., Hendeby, G., Bleser, G., Stricker, D. (2010). Activity Recognition Using Biomechanical Model Based Pose Estimation. In: Lukowicz, P., Kunze, K., Kortuem, G. (eds) Smart Sensing and Context. EuroSSC 2010. Lecture Notes in Computer Science, vol 6446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16982-3_4
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DOI: https://doi.org/10.1007/978-3-642-16982-3_4
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