Jules Françoise | Searcharxiv

arXiv2022

Entangling Practice with Artistic and Educational Aims: Interviews on Technology-based Movement Sound Interactions

Movement-sound interactive systems are at the interface of different artistic and educational practices. Within this multiplicity of uses, we examine common denominators in terms of learning, appropriation and relationship to technological systems. While these topics have been previously reported at NIME, we wanted to investigate how practitioners, coming from different perspectives, relate to these questions. We conducted interviews with 6 artists who are engaged in movementsound interactions: 1 performer, 1 performer/composer, 1 composer, 1 teacher/composer, 1 dancer/teacher, 1 dancer. Through a thematic analysis of the transcripts we identified three main themes related to (1) the mediating role of technological tools (2) usability and normativity, and (3) learning and practice. These results provide ground for discussion about the design and study of movement-sound interactive systems.

cs.HC↗

arXiv2020

Machine Learning Approaches For Motor Learning: A Short Review

Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of 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.

cs.LG↗