ニューラルネットワークによる歩行相識別で下肢外骨格を滑らかに透過制御
Identification of Gait Phases with Neural Networks for Smooth Transparent Control of a Lower Limb Exoskeleton
下肢外骨格の滑らかな透過制御のため、関節運動学に基づく歩行相分割をニューラルネットワークで非線形化し、線形回帰と比較した上でオンライン実装を被験者で検証した。
著者: Vittorio Lippi, Cristian Camardella, Alessandro Filippeschi, Francesco Porcini
分類: cs.RO
原文アブストラクト
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.
