Document Type thesis Author Name Keating, Jennifer Email Address jekeating at wpi.edu URN etd-043014-143646 Title Relating forearm muscle electrical activity to finger forces Degree MS Department Electrical & Computer Engineering Advisors 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/Defense 2014-04-30 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 . 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 , , . 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 , . Classification schemes for discriminating between hand/wrist functions and individual finger movements have demonstrated accuracy up to 95% , , . 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% MVC–6.59% 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.
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