Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-043014-143646

Document Typethesis
Author NameKeating, Jennifer
Email Address jekeating at wpi.edu
TitleRelating forearm muscle electrical activity to finger forces
DepartmentElectrical & Computer Engineering
  • Edward A. Clancy, Advisor
  • Gregory S. Fischer, Committee Member
  • Donald R. Brown, Committee Member
  • Keywords
  • system identification
  • EMG-Force model
  • EMG
  • proportional control
  • prosthetics
  • orthotics
  • Date of Presentation/Defense2014-04-30
    Availability unrestricted


    The electromyogram (EMG) signal is desired to be

    used as a control signal for applications such as

    multifunction prostheses, wheelchair navigation,

    gait generation, grasping control, virtual

    keyboards, and gesture-based interfaces [25].

    Several research studies have attempted to relate

    the electromyogram (EMG) activity of the forearm

    muscles to the mechanical activity of the wrist,

    hand and/or fingers [41], [42], [43]. A primary

    interest is for EMG control of powered upper-limb

    prostheses and rehabilitation orthotics. Existing

    commercial EMG-controlled devices are limited to

    rudimentary control capabilities of either

    discrete states (e.g. hand close/open), or one

    degree of freedom proportional control [4], [36].

    Classification schemes for discriminating between

    hand/wrist functions and individual finger

    movements have demonstrated accuracy up to 95%

    [38], [39], [29]. These methods may provide for

    increased amputee function, though continuous

    control of movement is not generally achieved.

    This thesis considered proportional control via

    EMG-based estimation of finger forces with the

    goal of identifying whether multiple degrees of

    freedom of proportional control information are

    available from the surface EMG of the forearm.

    Electromyogram (EMG) activity from the extensor

    and flexor muscles of the forearm was sensed with

    bipolar surface electrodes and related to the

    force produced at the four fingertips during

    constant-posture, slowly force-varying

    contractions from 20 healthy subjects. The

    contractions ranged between 30% maximum voluntary

    contractions (MVC) extension and 30% MVC flexion.

    EMG amplitude sampling rate, least squares

    regularization, linear vs. nonlinear models and

    number of electrodes used in the system

    identification were studied.

    Results are supportive that multiple degrees of

    freedom of proportional control information are

    available from the surface EMG of the forearm, at

    least in healthy subjects. An EMG amplitude

    sampling frequency of 4.096 Hz was found to

    produce models which allowed for good EMG

    amplitude estimates. Least squares regularization

    with a pseudo-inverse tolerance of 0.055 resulted

    in significant improvement in modeling results,

    with an average error of 4.69% MVC6.59% MVC

    (maximum voluntary contraction). Increasing

    polynomial order did not significantly improve

    modeling results. Results from smaller electrode

    arrays remained fairly good with as few as six

    electrodes, with the average %MVC error ranging

    from 5.13%7.01% across the four fingers. This

    study also identified challenges in the current

    experimental study design and subsequent system

    identification when EMG-force modeling is

    performed with four fingers simultaneously.

    Methods to compensate for these issues have been

    proposed in this thesis.

  • jekeating.pdf

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