Authors:
Vittorio Lippi
1
;
Cristian Camardella
2
;
Alessandro Filippeschi
3
;
2
and
Francesco Porcini
2
Affiliations:
1
University Hospital of Freiburg, Neurology, Freiburg, Germany
;
2
Scuola Superiore Sant’Anna, TeCIP Institute, PERCRO Laboratory, Pisa, Italy
;
3
Scuola Superiore Sant’Anna, Department of Excellence in Robotics and AI, Pisa, Italy
Keyword(s):
Wearable Robots, Neural Networks, Exoskeleton, Gait Phases.
Abstract:
Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject.