4 accepted papers
Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes
In addition to lifting up the body during gait, human legs provide stabilizing torques that can be modeled as a spring-damper mechanical impedance. While powered prosthetic leg actuators can also imitate spring-damper behaviors, the rendered impedance can be quite different from the desired impedanc
Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with patho-logical gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based c…
Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (<i>e</i>.<i>g</i>., speed, slope, and stride length) change constantly. One potential solution is to detect these