Abstract(s)
Human-robot interaction based on real-time kinematics or electromyography (EMG) feedback improves rehabilitation using assist-as-needed strategies. Muscle forces are
expected to provide even more comprehensive information than
EMG to control these assistive rehabilitation devices. Measuring
in vivo muscle force is challenging, leading to the development
of numerical methods to estimate them. Due to their high computational cost, forward dynamics-based optimization algorithms
were not viable for real-time estimation until recently. To achieve
muscle forces estimation in real time, a moving horizon estimator
(MHE) algorithm was used to track experimental biosignals. Two
participants were equipped with EMG sensors and skin markers
that were streamed in real time and used as targets for the MHE.
The upper-limb musculoskeletal (MSK) model was composed of
10 degrees-of-freedom actuated by 31 muscles. The MHE relies
on a series of overlapping trajectory optimization subproblems
of which the following parameters have been adjusted: the fixed
duration and the frame to export. We based this adjustment on
the estimation delay, the muscle saturation, the joint kinematic
mean power frequency, and errors to experimental data. Our
algorithm provided consistent estimates of muscle forces and
kinematics with visual feedback at 30 Hz with a 110 ms delay. This
method is promising to guide rehabilitation and enrich assistive
device control laws with personalized force estimations.