17844361. Method for Estimating Intention Using Unsupervised Learning simplified abstract (Hyundai Motor Company)

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Method for Estimating Intention Using Unsupervised Learning

Organization Name

Hyundai Motor Company

Inventor(s)

Muhammad Zahak Jamal of Yongin-si (KR)

Ju Young Yoon of Suwon-si (KR)

Dong Hyun Lee of Uiwang-si (KR)

Method for Estimating Intention Using Unsupervised Learning - A simplified explanation of the abstract

This abstract first appeared for US patent application 17844361 titled 'Method for Estimating Intention Using Unsupervised Learning

Simplified Explanation

The patent proposal describes a control scheme for a robot hand using myoelectric intention estimation of the human being. This is achieved through the use of the kernel Principal Component Analysis Algorithm (kPCA).

  • The system includes a biometric EMG sensor system, a robot hand with multiple fingers, a controller, and a robot hand.
  • The controller obtains the biometric EMG signal from the biometric sensor system.
  • The controller uses the kernel principal component analysis (kPCA) algorithm with a kernel function to estimate the myoelectric motion intention of the user.
  • Based on the estimated motion intention, the controller generates a control command.
  • The control command is then sent to the robot hand, allowing it to perform the desired motion.

Potential Applications

  • Prosthetic limbs: This technology can be used to develop advanced prosthetic hands that can be controlled by the user's muscle signals.
  • Robotic surgery: The control scheme can be applied to robotic surgical systems, allowing surgeons to control robotic arms with their muscle signals.
  • Industrial automation: The technology can be used in manufacturing and assembly lines, where robots can be controlled by human operators using their muscle signals.

Problems Solved

  • Limited control options: This technology solves the problem of limited control options for robotic hands by allowing users to control them using their muscle signals.
  • Natural and intuitive control: The use of myoelectric intention estimation provides a more natural and intuitive way of controlling robotic hands, mimicking the user's own hand movements.

Benefits

  • Enhanced functionality: The control scheme allows for a wider range of hand movements and gestures, providing enhanced functionality for prosthetic hands and robotic systems.
  • Improved user experience: By using muscle signals, users can control the robot hand more intuitively, leading to an improved user experience.
  • Increased precision: The myoelectric intention estimation enables precise control of the robot hand, allowing for more accurate and delicate movements.


Original Abstract Submitted

This patent proposal document provides a complete robot hand control scheme using myoelectric intention estimation of the human being using the kernel Principal Component Analysis Algorithm (kPCA). The robot hand system includes a biometric EMG sensor system, a robot hand including with multiple fingers, a controller connected with the biometric EMG sensor system, and a robot hand. The controller acquires the biometric EMG signal by means of a biometric sensor system, estimates myoelectric motion intention by applying the kernel principal component analysis (kPCA) algorithm using a kernel function, and delivers a control command corresponding to the estimated motion intention of the user to the robot hand.

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