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 |
|---|---|---|
| Contraction / Expansion 1 | Movement contracting or expanding over time. | |
| Directness 1 | Straight vs. flexible trajectory toward a target (Laban’s Space). | |
| Lightness 1 | Influence of gravity on movement (vertical vs. horizontal acceleration). | |
| Suddenness 1 | Rapid vs. sustained velocity changes (Laban’s Time). | |
| Impulsivity 1 | Abrupt movement without preparation by antagonist muscles. | |
| Equilibrium 1 | Stability or tendency to fall. | |
| Fluidity 1 | Smooth, wave-like propagation of movement across joints. | |
| Repetitiveness 1 | Recurrence of similar movement patterns. | |
| Tension 1 | Multi-plane rotations and spirals, derived from postural tension. | |
| Origin 1 | Leading joint in a movement; leadership dynamics in groups. | |
| Attraction 1 | Influence of external points in space (magnet-like). | |
| Slowness 1 | Sustained extremely slow motion. | |
| Stillness 1 | Minimal micro-movements (respiration, attention, emotion-driven). | |
| Fragility 1 | Vulnerability and delicacy in movement. |
References
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Piana, S., Alborno, P., Niewiadomski, R., Mancini, M., Volpe, G., & Camurri, A. (2016, May). 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). ↩↩↩↩↩↩↩↩↩↩↩↩↩↩
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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. ↩
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Niewiadomski, R., Mancini, M., Piana, S., Alborno, P., Volpe, G., & Camurri, A. (2017, November). Low-intrusive recognition of expressive movement qualities. In Proceedings of the 19th ACM international conference on multimodal interaction (pp. 230-237).) ↩
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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. ↩
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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. ↩
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Niewiadomski, R., Mancini, M., Volpe, G., & Camurri, A. (2015, September). Automated detection of impulsive movements in HCI. In Proceedings of the 11th Biannual Conference of the Italian SIGCHI Chapter (pp. 166-169). ↩