  The high prevalence of work-related musculoskeletal disorders (WMSDs) could
be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for
manufacturing applications. In this context, this paper proposes two hypotheses
for ergonomically effective task delegation and HRC. The first hypothesis
states that it is possible to quantify ergonomically professional tasks using
motion data from a reduced set of sensors. Then, the most dangerous tasks can
be delegated to a collaborative robot. The second hypothesis is that by
including gesture recognition and spatial adaptation, the ergonomics of an HRC
scenario can be improved by avoiding needless motions that could expose
operators to ergonomic risks and by lowering the physical effort required of
operators. An HRC scenario for a television manufacturing process is optimized
to test both hypotheses. For the ergonomic evaluation, motion primitives with
known ergonomic risks were modeled for their detection in professional tasks
and to estimate a risk score based on the European Assembly Worksheet (EAWS). A
Deep Learning gesture recognition module trained with egocentric television
assembly data was used to complement the collaboration between the human
operator and the robot. Additionally, a skeleton-tracking algorithm provided
the robot with information about the operator's pose, allowing it to spatially
adapt its motion to the operator's anthropometrics. Three experiments were
conducted to determine the effect of gesture recognition and spatial adaptation
on the operator's range of motion. The rate of spatial adaptation was used as a
key performance indicator (KPI), and a new KPI for measuring the reduction in
the operator's motion is presented in this paper.
