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Reference : UMR9015-JULFRA-005
Workplace : ST AUBIN
Date of publication : Friday, July 9, 2021
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 November 2021
Proportion of work : Full time
Remuneration : between 2000 and 2200 € net monthly salary according to experience
Desired level of education : PhD
Experience required : Indifferent
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.
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 . Complementary to analytical models of movement learning, the development of data-driven strategies using machine learning is a core focus of the ELEMENT project . 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 postdoctoral research project focuses on the design, implementation and evaluation of multisensory feedback systems to assist movement learning in various contexts. In particular, we will study how auditory or visual feedback can be used to support the learning of movement sequences. We consider sequences of multiple movement units (or gestures) which must be executed with specific properties (e.g. trajectory, dynamics, movement quality) and articulated together. Modeling such sequences requires the development of appropriate analysis techniques that can capture various properties of the movement, such as trajectories, dynamics, or even movement qualities [5, 6], as well as their relationship to movement learning.
According to the applicant's profile, the project could either focus on movement modeling with machine learning, in particular using neural networks, or on experimental studies to design and evaluate learning technologies.
The main research questions of the proposed postdoctoral project are:
- What representations of the movement can be combined to capture multiple properties of the movement (trajectories, dynamics, expressivity, articulation, etc.)?
- What computational models can quantify and track these properties as a function of the learning process of a person?
- How to design continuous multisensory feedback in order to support the learning of these various properties in a complex movement sequence?
- Can adaptive feedback systems improve the learning rate and learning experience?
- Conduct a state of the art on the field of adaptive methods in movement modeling, adaptive systems for movement learning, and auditory feedback for movement learning.
- Design and implement one or several interactive systems providing continuous audio feedback for movement learning. The design process will be iterative and will include intermediate evaluation workshops. Knowledge about movement skill acquisition from initial observations will be used to inform the design of the sonification strategies. The design will focus on integrating adaptation mechanisms to adapt the feedback to the expertise and learning process of the person.
- Design and conduct controlled experiments 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.
- Publications and dissemination.
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 strong 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 should have experience with research publications in the field of movement and computing (MOCO, CHI, DIS, TOCHI, JMUI, ...) and/or other movement-related conferences and journals.
This full time postdoctoral 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). It is expected that the post-doctoral fellow will interact strongly with doctoral students and post-doctoral fellows involved in the project, and will work in collaboration with all partners. Travel for experiments or conferences may be funded during the postdoc.
The post doctoral fellow will be provided 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.
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