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The Sit-To-Stand (STS) transition is a critical activity for maintaining physical fitness and independence among elderly individuals. Declining STS ability due to agerelated factors can lead to dependence and increased fall risk. This paper introduces the SkyWalker, a robotic rollator designed to provide adaptive STS trajectory motion support using advanced 3D visual pose estimation. Equipped with depth cameras and machine learning algorithms, the SkyWalker detects and responds to user movements in real-time, ensuring safe and smooth transitions from sitting to standing. Our vision- based approach employs the Google Mediapipe Human Skeleton algorithm to accurately capture user posture and classify STS phases, enabling the robot to dynamically adjust its STS trajectory based on user movements. Experimental results with seven participants demonstrate the effectiveness of our adaptive STS trajectory system in enhancing user stability and comfort compared to predefined trajectories. The SkyWalker’s adaptive assistance significantly improves synchronization of the robot movement with the user’s natural movements, reducing fall risk and promoting independence.

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Enabling Safe Sit-To-Stand Support with Mobile Robots: 3D Visual Pose Estimation in Close Quarters and Support Adaptation

  • 会议论文

  • 发表日期：

    2025年

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  • Anas Mahdi,
  • Zonghao Dong,
  • Jonathan Feng-Shun Lin,
  • Yue Hu,
  • Yasuhisa Hirata,
  • Katja Mombaur

摘要

The Sit-To-Stand (STS) transition is a critical activity for maintaining physical fitness and independence among elderly individuals. Declining STS ability due to agerelated factors can lead to dependence and increased fall risk. This paper introduces the SkyWalker, a robotic rollator designed to provide adaptive STS trajectory motion support using advanced 3D visual pose estimation. Equipped with depth cameras and machine learning algorithms, the SkyWalker detects and responds to user movements in real-time, ensuring safe and smooth transitions from sitting to standing. Our vision- based approach employs the Google Mediapipe Human Skeleton algorithm to accurately capture user posture and classify STS phases, enabling the robot to dynamically adjust its STS trajectory based on user movements. Experimental results with seven participants demonstrate the effectiveness of our adaptive STS trajectory system in enhancing user stability and comfort compared to predefined trajectories. The SkyWalker’s adaptive assistance significantly improves synchronization of the robot movement with the user’s natural movements, reducing fall risk and promoting independence.