Name
Abstract | ||
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Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable of exhibiting and recognizing in others’ movement. In sports (e.g., martial arts) and performing arts (e.g., dance), the same sequence of movements can be performed in a wide range of ways characterized by different qualities, often in terms of subtle (spatial and temporal) perturbations of the movement. Even a non-expert observer can distinguish between a top-level and average performance by a dancer or martial artist. The difference is not in the performed movements--the same in both cases--but in the “quality” of their performance.
In this article, we present a computational framework aimed at an automated approximate measure of movement quality in full-body physical activities. Starting from motion capture data, the framework computes low-level (e.g., a limb velocity) and high-level (e.g., synchronization between different limbs) movement features. Then, this vector of features is integrated to compute a value aimed at providing a quantitative assessment of movement quality approximating the evaluation that an external expert observer would give of the same sequence of movements.
Next, a system representing a concrete implementation of the framework is proposed. Karate is adopted as a testbed. We selected two different katas (i.e., detailed choreographies of movements in karate) characterized by different overall attitudes and expressions (aggressiveness, meditation), and we asked seven athletes, having various levels of experience and age, to perform them. Motion capture data were collected from the performances and were analyzed with the system. The results of the automated analysis were compared with the scores given by 14 karate experts who rated the same performances. Results show that the movement-quality scores computed by the system and the ratings given by the human observers are highly correlated (Pearson’s correlations r &equals 0.84, p &equals 0.001 and r &equals 0.75, p &equals 0.005).
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3132369 | ACM Transactions on Interactive Intelligent Systems (TiiS) |
Keywords | Field | DocType |
Gesture analysis, dance, full-body movement, karate, movement quality | Motion capture,Synchronization,Dance,Expression (mathematics),Computer science,Martial arts,Gesture analysis,Artificial intelligence,Quantitative assessment,Observer (quantum physics),Machine learning,Distributed computing | Journal |
Volume | Issue | ISSN |
9 | 1 | 2160-6455 |
Citations | PageRank | References |
2 | 0.40 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radoslaw Niewiadomski | 1 | 414 | 35.95 |
| Ksenia Kolykhalova | 2 | 10 | 2.71 |
| Stefano Piana | 3 | 157 | 16.11 |
| Paolo Alborno | 4 | 20 | 6.18 |
| Gualtiero Volpe | 5 | 864 | 101.42 |
| Antonio Camurri | 6 | 1107 | 142.92 |