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Analysis Primitives

Analysis primitives are operators applied across layers to extract meaningful patterns from features.
They summarize, transform, or model data at various temporal and spatial scales.

Example of Analysis Primitives

Primitive Type Description Implemented
Statistical Moments Unary operators summarizing distributions (mean, variance, skewness, kurtosis).
Shape Descriptors Peaks, slopes, valleys in time-series; geometric descriptors of movement curves.
Entropy 1 Approximate/sample entropy, recurrence analysis; quantify predictability or irregularity.
Time-Frequency Transforms Fourier or wavelet transforms to detect rhythm, periodicity, or temporal structures.
Symmetry 2 Unary/binary operators measuring geometric or dynamic balance (e.g., left vs. right entropy or energy).
Synchronization 34 Binary/n-ary operators measuring alignment of signals (cross-correlation, phase-locking, group entrainment).
Causality 4 Directional relationships (e.g., Granger causality, transfer entropy) to detect leader–follower dynamics.
Clusterability 5 Measures the tendency of data points to form clusters by means of the Hopkins statistics.
Predictive Models Hidden Markov Models, classifiers, neural networks; used for gesture segmentation or quality inference.
Saliency / Rarity 6 Detecting unusual occurrences in movement with respect to most frequent patterns.

References

  1. Glowinski, D., Mancini, M., & Camurri, A. (2013, March). Studying the effect of creative joint action on musicians’ behavior. In International Conference on Arts and Technology (pp. 113-119). Berlin, Heidelberg: Springer Berlin Heidelberg. ↩

  2. 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. ↩

  3. Varni, G., Volpe, G., & Camurri, A. (2010). A system for real-time multimodal analysis of nonverbal affective social interaction in user-centric media. IEEE Transactions on Multimedia, 12(6), 576-590. ↩

  4. Sabharwal, S. R., Varlet, M., Breaden, M., Volpe, G., Camurri, A., & Keller, P. E. (2022). huSync-A model and system for the measure of synchronization in small groups: A case study on musical joint action. IEEE Access, 10, 92357-92372. ↩↩

  5. Corbellini, N., Ceccaldi, E., Varni, G., & Volpe, G. (2022, August). An exploratory study on group potency classification from non-verbal social behaviours. In International Conference on Pattern Recognition (pp. 240-255). Cham: Springer Nature Switzerland. ↩

  6. 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. ↩