"Should I Lead?" Feasibility Study of User Perception on Following-Robot for Gait Assessment.

2023 21st International Conference on Advanced Robotics (ICAR)(2023)

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摘要
Current techniques of gait assessment rely on wearable sensors, pressure-sensitive walkway systems, and optical motion capture systems. A less invasive and more portable solution could be represented by a mobile robot that follows the user during the gait activity. The main idea behind this work relies on finding the best robot configuration for less invasive gait analysis with high acceptability from the end users. To this aim, two follow-me configurations have been designed: human-leader (i.e. the robot follows the person from behind), and robot-leader (i.e. robot follows the person from the front). We asked 27 young participants to test both modalities and to evaluate their perception of the robot in 5 domains: comfort, expected conformity, safety, trust, and unobtrusiveness. Additionally, we extracted quantitative parameters related to the walking experience from the data recorded by the platform and we analyzed them in tandem with the qualitative results. The results reported that robot-leader configuration tended to be more appreciated in terms of comfort, trust, and safety. On the contrary, the human-leader configuration is perceived as less obtrusive, less invasive, and in line with users' expectations. Considering the gait assessment application, we expect the human-leader configuration to return more promising and accurate results.
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关键词
Promising Results,Wearable Sensors,Mobile Robot,Gait Assessment,Robot Configuration,Perception Of The Robot,Optical Motion Capture System,Deep Learning,Signed-rank Test,Field Of View,Quantitative Evaluation,Velocity Profile,Difference In Latency,Proportional-integral-derivative,Linear Velocity,Safe Distance,Log Files,Target User,Robotic Assistance,Linear Path,Velocity Of The Robot,Safety Domain,User Comfort,Total Number Of Peaks,Space Occupancy,Walking Trajectory,Perception Module,Image Stream,Body Height,Quantitative Analysis
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