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Layer 3 – Mid-Level Features

Mid-level features capture structural properties of movement across units or time windows.
They operate at a higher abstraction than low-level descriptors, often integrating multiple signals into amodal features.

Key Concepts

  • Segmentation: movements are divided into units that depends on the context (e.g., technical gestures in sport, choreographic phrases) or analyzed over defined windows (e.g., 0.5s - 3s).
  • Amodal descriptors: features meaningful across modalities (e.g., movement and audio).
  • Trajectories in feature space: sequences of values describing movement dynamics in multidimensional spaces.

Examples of Mid-Level Features

Feature Description Implemented
Directness 1 Straight vs. flexible trajectory toward a target (Laban’s Space).
Lightness 23 Influence of gravity on movement (vertical vs. horizontal acceleration).
Impulsivity 45 Abrupt movement without preparation by antagonist muscles.
Fluidity 67 Smooth, wave-like propagation of movement across joints.
Fragility 23 Vulnerability and delicacy in movement.

References

  1. Piana, S., Staglianò, A., Camurri, A., & Odone, F. (2013). A set of full-body movement features for emotion recognition to help children affected by autism spectrum condition. In IDGEI International Workshop (Vol. 23). ↩

  2. Niewiadomski, R., Mancini, M., Piana, S., Alborno, P., Volpe, G., & Camurri, A. (2017). Low-intrusive recognition of expressive movement qualities. In Proceedings of the 19th ACM international conference on multimodal interaction (pp. 230-237).) ↩↩

  3. Niewiadomski, R., Mancini, M., Cera, A., Piana, S., Canepa, C., & Camurri, A. (2019). Does embodied training improve the recognition of mid-level expressive movement qualities sonification?. Journal on Multimodal User Interfaces, 13, 191-203. ↩↩

  4. 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. ↩

  5. Niewiadomski, R., Mancini, M., Volpe, G., & Camurri, A. (2015). Automated detection of impulsive movements in HCI. In Proceedings of the 11th Biannual Conference of the Italian SIGCHI Chapter (pp. 166-169). ↩

  6. Piana, S., Alborno, P., Niewiadomski, R., Mancini, M., Volpe, G., & Camurri, A. (2016). Movement fluidity analysis based on performance and perception. In Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems (pp. 1629-1636). ↩

  7. Alborno, P., Cera, A., Piana, S., Mancini, M., Niewiadomski, R., Canepa, C., Volpe G. & Camurri, A. (2016). Interactive sonification of movement qualities–a case study on fluidity. Proceedings of ISon, 35. ↩