Title: Collaborative robots as a tool for optimizing skill acquisition through the appropriate use of
\nmotor variability: Quantification of human motor variability in a constrained task
\nContext
\nOne of the major challenges of the so-called \u201cFactory of the Future\u201d is the improvement of workers\u2019
\noccupational health, and specifically the reduction of work-related musculoskeletal disorders
\n(WMSDs). WMSDs, indeed, have both an immediate and a long term impact on the quality of life
\nof workers, as well as a significant economic cost [1,2].
\nThe development of WMSDs is caused by the repetition of physically demanding tasks which
\ngenerate fatigue and, in the long term, damage body tissues (e.g., muscles, tendons, ligaments).
\nThus, physical fatigue and WMSDs are tightly coupled [3]. On the other hand, human motor
\nvariability appears to be a positive factor for delaying the onset of fatigue and\/or for counteracting
\nits effects [4]. Exploiting motor variability corresponds to using different motor strategies, at joint
\nand\/or at muscle level, to perform a given task. One can thereby reduce muscle\/joint prolonged
\noverloading by switching between different motor strategies, giving the fatigued muscle\/joint time
\nto recover. Enabling and encouraging industrial workers to positively exploit the intrinsic variability
\nof the task (not all degrees of freedom of the tasks are constrained) [5], combined with their own
\nmotor variability (due to the kinematic and actuation redundancy of the human body) [6], could
\ntherefore help reduce WMSDs. Such exploitation of motor variability is, ideally, the natural result
\nachieved by an expert. Such optimum is, however, not always reached, or requires a very long
\npractice time. Methodologies to help the acquisition of motor habits exploiting at best the overall
\nvariability therefore need to be developed. In this respect, collaborative robots are a possible tool
\nthat could enable an individualized acquisition of such good practices at the motor level [7].
\nObjectives and Work Plan
\nWithin the context described above, the internship focuses on the quantification of human motor
\nvariability in a partially constrained task. The methods and tools developed during the internship
\nwill be applied to the case of a trajectory tracking task, representative of industrial tasks typically
\nobserved on assembly lines.
\nThe main objectives of the internship are:
\n\uf0b7 Identify approaches in the literature which allow to quantify both the variability associated
\nwith a given task, and the motor variability of a human operator subjected to task-related
\nkinematic constraints.
\n\uf0b7 Develop and conduct an experiment to compare the motor variability observed in humans
\nduring a partially constrained trajectory tracking task, to the theoretical variability computed
\nusing a digital human model. The goal of this experiment is to estimate how much humans,
\npossibly with different levels of expertise, exploit the available variability.
\n\uf0b7 Analyze the relation between the level of constraints imposed by the task and the motor
\nvariability exhibited by human operators.
\nAdvising and Organization
\nThe internship will be co-supervised by:
\n\uf0b7 Pauline Maurice (CNRS Researcher in Larsen team at LORIA, Nancy):
\npauline.maurice@loria.fr,
\n\uf0b7 Jonathan Savin (Research Engineer at INRS, Nancy): jonathan.savin@inrs.fr,
\n\uf0b7 Vincent Padois (Research scientist in Auctus team at INRIA Bordeaux Sud-Ouest):
\nvincent.padois@inria.fr,
\n\uf0b7 David Daney (INRIA researcher in Auctus team at INRIA Bordeaux Sud-Ouest):
\ndavid.daney@inria.fr.
\nThe internship is for a duration of 6 months, in the interval between January and September 2020.
\nThe intern will be mainly located in LORIA\/INRIA research center in Nancy.
\nThis internship is preliminary to a potential PhD thesis continuing the same topic (collaborative
\nrobot as a tool for optimizing skill acquisition through the appropriate use of motor variability).
\nRequirements
\nTechnical skills: Robotics, signal theory, statistical analysis, Matlab\/Python\/C++ programming.
\nExperience with human subject experiment and\/or motion capture is a plus.
\nGeneral Skills: Team player, autonomous, proactive, creative, enthusiastic, organized, serious and
\nrigorous (this list is not exhaustive).
\nLanguage: English or French
\nApplication
\nApplicants should send their CV, motivation letter describing their specific interest for the topic,
\nand their Master’s grades to the aforementioned advising team.<\/p>\n