  Human impedance parameters play an integral role in the dynamics of strength
amplification exoskeletons. Many methods are used to estimate the stiffness of
human muscles, but few are used to improve the performance of strength
amplification controllers for these devices. We propose a compliance shaping
amplification controller incorporating an accurate online human stiffness
estimation from surface electromyography (sEMG) sensors and stretch sensors
connected to the forearm and upper arm of the human. These sensor values along
with exoskeleton position and velocity are used to train a random forest
regression model that accurately predicts a person's stiffness despite varying
movement, relaxation, and muscle co-contraction. Our model's accuracy is
verified using experimental test data and the model is implemented into the
compliance shaping controller. Ultimately we show that the online estimation of
stiffness can improve the bandwidth and amplification of the controller while
remaining robustly stable.
