This paper presents a novel sensor fusion methodology
to dynamically detect weight variations and the position of
an exoskeleton system. The proposed methodology is intended
for tasks of lifting and lowering heavy weights with an industrial
exoskeleton to substantially reduce spinal loads during these
activities.
Instead of extensively placing force sensors by covering the
whole plantar area, as most of commercial applications do, we
integrate only a few force sensors in specific plantar area, so
that the sensory system is not restrict to individual foot size
and shape, and on the other hand has relatively lower material
cost. Thanks to the fusion scheme with the inertial measurement
unit (IMU) we are able to overcome the acceleration force and
precisely detect the lifting load.
Industrial exoskeletons are intended to assist workers when
handling heavy goods.With this in mind, wearers are not able to
use their hands to control the exoskeleton since they use them
to handle the goods. Therefore, the exoskeleton controller is
required to indirectly infer when the wearer requires assistance
for lifting or lowering a heavy weight. Our approach of
dynamically detect the increment/decrement of weight, as well
as the rising/falling edge, enables the exoskeleton’s controller
to trigger the request of assistive force to the actuators.
In addition to the real experiments, our research includes the
development of a virtual environment able to model commercial
force sensors and able to simulate an industrial setting with
a human operator, exoskeleton and different heavy goods to
analyze the sensors response. We present results from the
simulation and from the real experiment that corroborate our
proposed methodology.