Expressive Movement Qualities Research Articles

Does embodied training improve the recognition of mid-level expressive movement qualities sonification?

This research is a part of a broader project exploring how movement qualities can be recognized by means of the auditory channel: can we perceive an expressive full-body movement quality by means of its interactive sonification? The paper presents a sonification framework and an experiment to evaluate if embodied sonic training (i.e., experiencing interactive sonification of your own body movements) increases the recognition of such qualities through the auditory channel only, compared to a non-embodied sonic training condition. We focus on the sonification of two mid-level movement qualities: fragility and lightness. We base our sonification models, described in the first part, on the assumption that specific compounds of spectral features of a sound can contribute to the cross-modal perception of a specific movement quality. The experiment, described in the second part, involved 40 participants divided into two groups (embodied sonic training vs. no training). Participants were asked to report the level of lightness and fragility they perceived in 20 audio stimuli generated using the proposed sonification models. Results show that (1) both expressive qualities were correctly recognized from the audio stimuli, (2) a positive effect of embodied sonic training was observed for fragility but not for lightness. The paper is concluded by the description of the artistic performance that took place in 2017 in Genoa (Italy), in which the outcomes of the presented experiment were exploited.

Toward a new cognitive neuroscience: modeling natural brain dynamics.

Toward a new cognitive neuroscience: modeling natural brain dynamics.

Neural decoding of expressive human movement from scalp electroencephalography (EEG).

Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.