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Vision-Based Recognition of Human Motion Intent during Staircase Approaching.

Md Rafi Islam1, Md Rejwanul Haque2, Masudul H Imtiaz3

  • 1Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.

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Summary
This summary is machine-generated.

This study introduces a vision-based system to predict if someone will climb stairs using head-mounted cameras. The method accurately recognizes motion intent for assistive robots before movement changes.

Keywords:
YOLOv5gradient boost classifierintent recognitionstaircase detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Real-world navigation requires continuous decision-making, such as choosing to ascend or avoid stairs.
  • Recognizing motion intent for assistive robot control (e.g., prosthetic limbs) is crucial but difficult due to limited information.

Purpose of the Study:

  • To develop a novel vision-based method for recognizing an individual's motion intent when approaching a staircase.
  • To enable assistive robots to anticipate and adapt to user intentions before a change in motion mode occurs.

Main Methods:

  • Utilized egocentric images from a head-mounted camera.
  • Trained a YOLOv5 object detection model to identify staircases.
  • Developed an AdaBoost and gradient boost (GB) classifier to determine intent (engage or avoid stairs).

Main Results:

  • Achieved reliable motion intent recognition with 97.69% accuracy.
  • Successfully predicted intent at least two steps before the potential transition to stair climbing.
  • Demonstrated the method's effectiveness in providing timely information for robot controller adjustments.

Conclusions:

  • The proposed vision-based approach reliably predicts stair-climbing intent using egocentric vision.
  • This method offers a significant advancement for assistive robot control, enhancing real-world usability.
  • Early intent recognition allows for proactive adjustments in assistive robotic systems.