Layer 2 – Low-Level Features
Low-level features are instantaneous descriptors of movement, usually computed directly from raw data (Layer 1) or from short sliding windows of samples.
They are typically represented as time-series with the same sampling rate as the input signals.
Examples of Low-Level Features
| Feature | Description | Implemented |
|---|---|---|
| Kinectic Energy / Quantity of Motion (QoM) 12 | Energy of a cloud of moving joints, weighted by their masses or area of the difference between consecutive silhouettes in consecutive frames | |
| Postural Contraction 12 | Extent to which body posture is close to its barycenter. | |
| Smoothness 3 | Motion of a joint according to biomechanics laws of smoothness. | |
| Equilibrium 4 | Projection of the body’s barycenter onto the floor within the support area of the feet | |
| Postural Tension 5 | Vector describing angular relations between feet, hips, trunk, shoulders, and head; inspired by angles in classical painting/sculpture used to express tension. |
References
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Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., & Scherer, K. (2011). Toward a minimal representation of affective gestures. IEEE Transactions on Affective Computing, 2(2), 106-118. ↩↩
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Camurri, A., Lagerlöf, I., & Volpe, G. (2003). Recognizing emotion from dance movement: comparison of spectator recognition and automated techniques. International journal of human-computer studies, 59(1-2), 213-225. ↩↩
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Mazzarino, B., & Mancini, M. (2009). The need for impulsivity & smoothness: improving hci by qualitatively measuring new high-level human motion features. In Proceedings of the International Conference on Signal Processing and Multimedia Applications (IEEE sponsored). ↩
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Ghisio, S., Coletta, P., Piana, S., Alborno, P., Volpe, G., Camurri, A., ... & Ravaschio, A. (2015, June). An open platform for full body interactive sonification exergames. In 2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN) (pp. 168-175). IEEE. ↩
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Camurri, Volpe, Piana, Mancini, Alborno, Ghisio (2018) The Energy Lift: automated measurement of postural tension and energy transmission. Proc. MOCO 2018 ↩