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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 = 0.84, p = 0.001 and r = 0.75, p = 0.005).

We present non-intrusive visual observation and estimation of movement parameters using RGB data for detecting the effect of olfactory stimulation (essential oils) on movement patterns of high school students during the lessons. In particular, we examine the effect of exposure to aromatherapy has on students’ movement kinetics of upper-body: velocity, acceleration, jerk and energy. The Lavender essential oil was used because of antiseptic, antimicrobial, anti-inflammatory and calming properties that may be used for treating anxiety, insomnia and depression [8, 11, 12]. Two classes were studied, as control and experimental group during two days with week of pause in between. First group had both days without aromatherapy, instead the second - two settings without and with aromatherapy for separate days. For post processing of the recorded data we use OpenPose [7] for estimation of position of joints, Matlab for processing positional data and tracking of the subjects, EyesWeb XMI for the extraction of movement features at a small time scale. Data showed significant differences in velocity, acceleration and jerk for left shoulder and elbow joints of experimental group in comparison between aroma and no aroma settings with Mann-Whitney U test at p < .05. In conclusion, this is an ongoing study shows the possibility of using movement qualities, such as kinematic movement features, extracted ecologically using non-invasive equipment, as a method to measure change of movement behavior, in the cases when no other type of data capture is possible. Future studies will involve further experiments and wider collection of movement features with higher level notations as fluidity, smoothness, rigidity of the movements.

In this paper we present a pilot study on non-intrusive visual observation and estimation of affective parameter using recorded videos (RGB). We aim at estimating student engagement analyzing upper-body movement comparing two different classroom settings: with the introduction of aromatherapy during the class vs standard lesson. Following previous studies on how aromatherapy can alter movement behaviour, we chose Lavender essential oil. We used computer vision techniques for pose estimation and developed software modules for the extraction of movement features from media data. Data show significant increases in overall velocity and acceleration when the participants are exposed to the aromatherapy condition. Significant decreases in neck flexion angle has been also observed, that shows students had a straighter head posture (i.e. sitting up straighter). No significant differences were observed for the overall kinetic energy of the joints and spinal extension.

A multimodal dataset is presented, which has been collected for analyzing and measuring the quality of movement performed during sport activities. Martial arts (namely karate) are taken as test-beds for investigation. Karate encompasses predefined sequences of movements (“katas”) that can be carried out with different qualities, e.g., by performers at different skill levels (highly vs. poorly skilled). The experimental setup and method are described. The dataset is composed of motion capture (MoCap) data, synchronized with video and audio recordings, of several participants with different levels of experience. The raw MoCap data are independent of any particular post-processing algorithm and can be used for other research purposes. In the second part of the paper, a set of measures is proposed to evaluate a kata performance. They are based on the geometrical and kinematic features, such as posture correctness and synchronization between limbs. and were chosen according to karate experts' opinion.

A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and non-physical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as "bridges" between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Preliminary results are presented.

In this work, a computational method is proposed to automatically investigate the perception of the origin of full-body human movement and its propagation. The method is based on a mathematical game built over a suitably defined graph structure representing the human body. The players of this game are the graph vertices, which form a subset of body joints. Since each vertex contributes to a shared goal (i.e., to the way in which a specific movement-related feature is transferred among the joints), a cooperative game-theoretical model (specifically a transferable-utility game) is adopted, which is able (via the Shapley value) to measure the relevance of the various joints in human movement when performing full-body movement analysis. The method is theoretically investigated and applied to a motion capture dataset obtained from subjects who performed expressive movements. Finally, the method is validated through an online survey, in which several dancers/nondancers participated. The results show the capability of the proposed approach to represent the evolution of the most important joint responsible for originating each dancer's movement.