En poursuivant votre navigation sur ce site, vous acceptez le dépôt de cookies dans votre navigateur. (En savoir plus)
Portail > Offres > Offre UMR9015-JULFRA-003 - Ingénieur·e H/F au LISN : Étude expérimentale de l'apprentissage du mouvement avec un retour auditif

Engineer Position at LISN: Experimental Study of Movement Learning with Auditory Feedback

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : mardi 3 août 2021

Assurez-vous que votre profil candidat soit correctement renseigné avant de postuler. Les informations de votre profil complètent celles associées à chaque candidature. Afin d’augmenter votre visibilité sur notre Portail Emploi et ainsi permettre aux recruteurs de consulter votre profil candidat, vous avez la possibilité de déposer votre CV dans notre CVThèque en un clic !

Faites connaître cette offre !

General information

Reference : UMR9015-JULFRA-003
Workplace : ST AUBIN
Date of publication : Tuesday, July 13, 2021
Type of Contract : FTC Technical / Administrative
Contract Period : 12 months
Expected date of employment : 1 November 2021
Proportion of work : Full time
Remuneration : between 1728 € and 2000 € net monthly salary, according to experience
Desired level of education : 5-year university degree
Experience required : Indifferent

Missions

Context: ANR Project ELEMENT
==========================================

Because memorizing and executing gestures is challenging for users, most current approaches to movement-based interaction consider intuitive interfaces and trivial gesture vocabularies. While these facilitate adoption, they also limit users' potential for more complex, expressive and truly embodied interaction. Considering movement-based interaction beyond the mouse-keyboard paradigm, the ANR project ELEMENT (Enabling Learnability in Embodied Movement Interaction) proposes to shift the focus from intuitiveness towards learnability: new interaction paradigms require users to develop specific sensorimotor skills compatible with – and transferable between, – digital interfaces (including video interface, mobile devices, internet of things, game interfaces). With learnable interactions, novice users should be able to approach a new system with a difficulty adapted to their expertise, then the system should be able to carefully adapt to the improving motor skills, and eventually enable complex, expressive and engaging interactions. The long-term aim is to foster innovation in multimodal interaction applied to different fields, from assistive technologies to media interaction in creative applications.

Goals
==========================================
The use of continuous multisensory feedback as a medium to support movement learning is showing promise in a number of applications such as sports, performing arts or rehabilitation [1, 2]. Such motor learning support systems require fine-grained movement modeling to provide appropriate audio, visual or haptic feedback to assist the learner in acquiring or recovering particular motor skills. In this context, there is a need for advanced computational models able to take into account sensorimotor learning mechanisms [3]. Complementary to analytical models of movement learning, the development of data-driven strategies using machine learning is a core focus of the ELEMENT project [4]. Such models have the potential to dynamically adapt to the skill level of the learner (relative to a given task) so as to provide appropriate feedback.
This engineer position will focus on setting up and running experimental studies to evaluate movement learning with multisensory feedback systems. The engineer will contribute to the development and implementation of experimental setups, using existing machine learning models, movement analysis methods and sonification technologies. The engineer will contribute to developing the experimental protocol and will run studies with participants.

Activities

- Design and implement one or several interactive systems providing continuous audio feedback for movement learning. Designs will focus on integrating adaptation mechanisms to adapt the feedback to the expertise and learning process of the person.
- Design and conduct experimental studies to evaluate the proposed system. The studies should assess the effectiveness of the feedback system to help acquire the various properties of the movement sequence.

Skills

We are looking for passionate candidates with scientific curiosity, good problem solving skills, and a strong background in one or several of the following domains: signal processing, machine learning, HCI. Candidates should have a solid background in the field of movement & computing, particularly in the analysis and processing of motion signals, and in the design and evaluation of motion-based interactive systems. Experience in creative application contexts such as dance or performing arts, and/or an interest in working on interaction for disability are appreciated.

Candidates must hold a Master's degree in computer science with a major in HCI or machine learning at the time of appointment. Candidates should be proficient in at least one programming language (preferably Python, JavaScript), and/or an interactive environment such as Max/MSP.

Work Context

This full time engineer position will take place at LISN as part of the ANR ELEMENT project. The ELEMENT project is coordinated by Ircam (Paris), and also involves two teams at LISN (Orsay). The candidate is expected to interact with all partners of the project.

The candidate will be equipped with a laptop computer and accessories. LISN has acquired several portable motion sensors (IMU, EMG sensors), and has a whole-body motion capture system. Additional equipment can be acquired during the project.

We talk about it on Twitter!