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Abstract Background Movement sonification, the use of real-time auditory feedback linked to movement parameters, have been proposed to support rehabilitation. Nevertheless, if promising results have been reported, the effect of the type of sound used has not been studied systematically. The aim of this study was to investigate in a single session the effect of different types of sonification both quantitatively and qualitatively on patients with acquired brain lesions and healthy participants. Methods An experimental setup enabling arm sonification was developed using three different categories of sonification (direct sound modulation, musical interaction, and soundscape). Simple moving forward movements performed while sliding on a table with both arms were investigated with all participants. Quantitative analysis on the movement timing were performed considering various parameters (sound condition, affected arm and dominance, sonification categories). Qualitative analysis of semi-structured interviews were also conducted, as well as neuropsychological evaluation of music perception. Results For both the patient and healthy groups (15 participants each), average duration for performing the arm movement is significantly longer with sonification compared to the no-sound condition (p

Abstract The effects of music on bodily movement and feelings, such as when people are dancing or engaged in physical activity, are well-documented—people may move in response to the sound cues, feel powerful, less tired. How sounds and bodily movements relate to create such effects? Here we deconstruct the problem and investigate how different auditory features affect people’s body-representation and feelings even when paired with the same movement. In three experiments, participants executed a simple arm raise synchronised with changing pitch in simple tones (Experiment 1), rich musical sounds (Experiment 2) and within different frequency ranges (Experiment 3), while we recorded indirect and direct measures on their movement, body-representations and feelings. Changes in pitch influenced people’s general emotional state as well as the various bodily dimensions investigated—movement, proprioceptive awareness and feelings about one’s body and movement. Adding harmonic content amplified the differences between ascending and descending sounds, while shifting the absolute frequency range had a general effect on movement amplitude, bodily feelings and emotional state. These results provide new insights in the role of auditory and musical features in dance and exercise, and have implications for the design of sound-based applications supporting movement expression, physical activity, or rehabilitation.

Analysing movement learning can rely on human evaluation, e.g. annotating video recordings, or on computing means in applying metrics on behavioural data. However, it remains challenging to relate human perception of movement similarity to computational measures that aim at modelling such similarity. In this paper, we propose a metric learning method bridging the gap between human ratings of movement similarity in a motor learning task and computational metric evaluation on the same task. It applies metric learning on a Dynamic Time Warping algorithm to derive an optimal set of movement features that best explain human ratings. We evaluated this method on an existing movement dataset, which comprises videos of participants practising a complex gesture sequence toward a target template, as well as the collected data that describes the movements. We show that it is possible to establish a linear relationship between human ratings and our learned computational metric. This learned metric can be used to describe the most salient temporal moments implicitly used by annotators, as well as movement parameters that correlate with motor improvements in the dataset. We conclude with possibilities to generalise this method for designing computational tools dedicated to movement annotation and evaluation of skill learning.