17844361. Method for Estimating Intention Using Unsupervised Learning simplified abstract (Kia Corporation)

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Method for Estimating Intention Using Unsupervised Learning

Organization Name

Kia Corporation

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.

  • The system includes a biometric EMG sensor system, a robot hand with multiple fingers, a controller, and a robot hand.
  • The controller collects the biometric EMG signal from the sensor system.
  • The controller uses the kernel Principal Component Analysis Algorithm (kPCA) to estimate the user's myoelectric motion intention.
  • The estimation is done by applying a kernel function to the EMG signal.
  • The controller then sends a control command to the robot hand based on the estimated motion intention.

Potential Applications

  • Prosthetic limbs: This technology can be used to control prosthetic hands, allowing users to perform complex movements and tasks.
  • Robotic surgery: The control scheme can be applied to robotic surgical instruments, enabling precise and intuitive control during procedures.
  • Industrial automation: The robot hand control scheme can be used in manufacturing and assembly lines to improve efficiency and accuracy.

Problems Solved

  • Limited control options: Traditional prosthetic hands have limited control capabilities, making it difficult for users to perform complex tasks.
  • Lack of intuitive control: Existing control schemes for robotic hands may require extensive training and are not intuitive for users.
  • Inefficient automation: Current industrial automation systems may lack the ability to adapt to different tasks and require manual programming.

Benefits

  • Enhanced functionality: The myoelectric intention estimation allows for more natural and precise control of the robot hand, improving the user's ability to perform various tasks.
  • Intuitive control: By using the user's own muscle signals, the control scheme provides a more intuitive and seamless control experience.
  • Adaptability: The use of the kPCA algorithm allows the system to adapt to different users and their specific motion intentions, increasing versatility and usability.


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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