Human-Exoskeleton Interaction Portrait
CoRR(2024)
摘要
Human-robot physical interaction contains crucial information for optimizing
user experience, enhancing robot performance, and objectively assessing user
adaptation. This study introduces a new method to evaluate human-robot
co-adaptation in lower limb exoskeletons by analyzing muscle activity and
interaction torque as a two-dimensional random variable. We introduce the
Interaction Portrait (IP), which visualizes this variable's distribution in
polar coordinates. We applied this metric to compare a recent torque controller
(HTC) based on kinematic state feedback and a novel feedforward controller
(AMTC) with online learning, proposed herein, against a time-based controller
(TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and
AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced
user-exoskeleton coordination. IP analysis reveals this improvement stems from
two distinct co-adaptation strategies, unidentifiable by traditional muscle
activity or interaction torque analyses alone. HTC encourages users to yield
control to the exoskeleton, decreasing muscular effort but increasing
interaction torque, as the exoskeleton compensates for user dynamics.
Conversely, AMTC promotes user engagement through increased muscular effort and
reduced interaction torques, aligning it more closely with rehabilitation and
gait training applications. IP phase evolution provides insight into each
user's interaction strategy development, showcasing IP analysis's potential in
comparing and designing novel controllers to optimize human-robot interaction
in wearable robots.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要