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Related Experiment Videos

Hand force estimation using Fast Orthogonal Search.

J Mikael Eklund1, Farid Mobasser, K Hashtrudi-Zaad

  • 1Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA. eklund@eecs.berkeley.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
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This study estimates human hand force using electromyogram (EMG) signals and elbow position. The Fast Orthogonal Search (FOS) method offers a rapid and accurate alternative to expensive force sensors.

Area of Science:

  • Biomechanics
  • Robotics
  • Signal Processing

Background:

  • Estimating human hand force is crucial for applications like prosthetics and human-robot interaction.
  • Traditional force sensors are often expensive, bulky, and impractical for widespread use.
  • Electromyogram (EMG) signals and joint position offer a potentially more accessible alternative.

Purpose of the Study:

  • To present the Fast Orthogonal Search (FOS) method for estimating human hand force.
  • To validate the efficacy of FOS using EMG and elbow angular position data.
  • To demonstrate a cost-effective and portable approach for force estimation.

Main Methods:

  • Development of a single degree-of-freedom robotic experimental testbed.
  • Collection of synchronized EMG and elbow angular position data.

Related Experiment Videos

  • Application of the Fast Orthogonal Search (FOS) algorithm for model identification.

    • Main Results:

    • The FOS method rapidly identified EMG-force models.
    • Achieved accuracy comparable to or better than existing methods.
    • Demonstrated the feasibility of using inexpensive EMG and position sensors.

    Conclusions:

    • FOS is a highly efficient and accurate method for estimating human hand force from EMG and position data.
    • This approach offers a practical and cost-effective solution for various applications.
    • Further development could lead to advanced human-robot interfaces and assistive devices.