2024/03/11 by Mohammad Shushtari, Shushtari, Mohammad, Julia Foellmer +3 · 1 citation
Engineering · Medicine · #Artificial Intelligence in Healthcare and Education #FOS: Computer and information sciences #Medical Imaging and Analysis #Robotics (cs.RO) #Stroke Rehabilitation and Recovery
paper · pdf · doi:10.48550/arxiv.2403.06851
openalex publication_date 2024/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.