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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.

Machine learning approaches have seen a considerable number of applications in human movement modeling but remain limited for motor learning. Motor learning requires that motor variability be taken into account and poses new challenges because the algorithms need to be able to differentiate between new movements and variation in known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.

Adults readily make associations between stimuli perceived consecutively through different sense modalities, such as shapes and sounds. Researchers have only recently begun to investigate such correspondences in infants but only a handful of studies have focused on infants less than a year old. Are infants able to make cross-sensory correspondences from birth? Do certain correspondences require extensive real-world experience? Some studies have shown that newborns are able to match stimuli perceived in different sense modalities. Yet, the origins and mechanisms underlying these abilities are unclear. The present paper explores these questions and reviews some hypotheses on the emergence and early development of cross-sensory associations and their possible links with language development. Indeed, if infants can perceive cross-sensory correspondences between events that share certain features but are not strictly contingent or co-located, one may posit that they are using a “sixth sense” in Aristotle’s sense of the term. And a likely candidate for explaining this mechanism, as Aristotle suggested, is movement.

Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a dissociable effect of practice variability on learning complex skills that involve both motor and timing constraints.

Communicating an auditory experience with words is a difficult task and, in consequence, people often rely on imitative non-verbal vocalizations and gestures. This work explored the combination of such vocalizations and gestures to communicate auditory sensations and representations elicited by non-vocal everyday sounds. Whereas our previous studies have analyzed vocal imitations, the present research focused on gestural depictions of sounds. To this end, two studies investigated the combination of gestures and non-verbal vocalizations. A first, observational study examined a set of vocal and gestural imitations of recordings of sounds representative of a typical everyday environment (ecological sounds) with manual annotations. A second, experimental study used non-ecological sounds whose parameters had been specifically designed to elicit the behaviors highlighted in the observational study, and used quantitative measures and inferential statistics. The results showed that these depicting gestures are based on systematic analogies between a referent sound, as interpreted by a receiver, and the visual aspects of the gestures: auditory-visual metaphors. The results also suggested a different role for vocalizations and gestures. Whereas the vocalizations reproduce all features of the referent sounds as faithfully as vocally possible, the gestures focus on one salient feature with metaphors based on auditory-visual correspondences. Both studies highlighted two metaphors consistently shared across participants: the spatial metaphor of pitch (mapping different pitches to different positions on the vertical dimension), and the rustling metaphor of random fluctuations (rapidly shaking of hands and fingers). We interpret these metaphors as the result of two kinds of representations elicited by sounds: auditory sensations (pitch and loudness) mapped to spatial position, and causal representations of the sound sources (e.g. rain drops, rustling leaves) pantomimed and embodied by the participants' gestures.