Stage M2 Recherche – Nancy

Title: Collaborative robots as a tool for optimizing skill acquisition through the appropriate use of
motor variability: Quantification of human motor variability in a constrained task
Context
One of the major challenges of the so-called “Factory of the Future” is the improvement of workers’
occupational health, and specifically the reduction of work-related musculoskeletal disorders
(WMSDs). WMSDs, indeed, have both an immediate and a long term impact on the quality of life
of workers, as well as a significant economic cost [1,2].
The development of WMSDs is caused by the repetition of physically demanding tasks which
generate fatigue and, in the long term, damage body tissues (e.g., muscles, tendons, ligaments).
Thus, physical fatigue and WMSDs are tightly coupled [3]. On the other hand, human motor
variability appears to be a positive factor for delaying the onset of fatigue and/or for counteracting
its effects [4]. Exploiting motor variability corresponds to using different motor strategies, at joint
and/or at muscle level, to perform a given task. One can thereby reduce muscle/joint prolonged
overloading by switching between different motor strategies, giving the fatigued muscle/joint time
to recover. Enabling and encouraging industrial workers to positively exploit the intrinsic variability
of the task (not all degrees of freedom of the tasks are constrained) [5], combined with their own
motor variability (due to the kinematic and actuation redundancy of the human body) [6], could
therefore help reduce WMSDs. Such exploitation of motor variability is, ideally, the natural result
achieved by an expert. Such optimum is, however, not always reached, or requires a very long
practice time. Methodologies to help the acquisition of motor habits exploiting at best the overall
variability therefore need to be developed. In this respect, collaborative robots are a possible tool
that could enable an individualized acquisition of such good practices at the motor level [7].
Objectives and Work Plan
Within the context described above, the internship focuses on the quantification of human motor
variability in a partially constrained task. The methods and tools developed during the internship
will be applied to the case of a trajectory tracking task, representative of industrial tasks typically
observed on assembly lines.
The main objectives of the internship are:
 Identify approaches in the literature which allow to quantify both the variability associated
with a given task, and the motor variability of a human operator subjected to task-related
kinematic constraints.
 Develop and conduct an experiment to compare the motor variability observed in humans
during a partially constrained trajectory tracking task, to the theoretical variability computed
using a digital human model. The goal of this experiment is to estimate how much humans,
possibly with different levels of expertise, exploit the available variability.
 Analyze the relation between the level of constraints imposed by the task and the motor
variability exhibited by human operators.
Advising and Organization
The internship will be co-supervised by:
 Pauline Maurice (CNRS Researcher in Larsen team at LORIA, Nancy):
pauline.maurice@loria.fr,
 Jonathan Savin (Research Engineer at INRS, Nancy): jonathan.savin@inrs.fr,
 Vincent Padois (Research scientist in Auctus team at INRIA Bordeaux Sud-Ouest):
vincent.padois@inria.fr,
 David Daney (INRIA researcher in Auctus team at INRIA Bordeaux Sud-Ouest):
david.daney@inria.fr.
The internship is for a duration of 6 months, in the interval between January and September 2020.
The intern will be mainly located in LORIA/INRIA research center in Nancy.
This internship is preliminary to a potential PhD thesis continuing the same topic (collaborative
robot as a tool for optimizing skill acquisition through the appropriate use of motor variability).
Requirements
Technical skills: Robotics, signal theory, statistical analysis, Matlab/Python/C++ programming.
Experience with human subject experiment and/or motion capture is a plus.
General Skills: Team player, autonomous, proactive, creative, enthusiastic, organized, serious and
rigorous (this list is not exhaustive).
Language: English or French
Application
Applicants should send their CV, motivation letter describing their specific interest for the topic,
and their Master’s grades to the aforementioned advising team.