Document Type thesis Author Name Liu, Pu URN etd-042711-124752 Title Effect of Joint Angle on EMG-Torque Model During Constant-Posture, Quasi-Constant-Torque Contractions Degree MS Department Electrical & Computer Engineering Advisors Edward A. Clancy, Advisor D. Richard Brown III, Committee Member Denis Rancourt, Committee Member Keywords EMG amplitude estimation. Electromyography biological system modeling joint angle influence EMG signal processing Date of Presentation/Defense 2011-04-27 Availability unrestricted
The electrical activity of skeletal muscle¡ªthe electromyogram (EMG)¡ªis of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system and peripheral joints and muscles. This thesis mainly describes an experimental study which relates the simultaneous biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (ranging from 45¡ãto 135¡ã) during constant-posture, quasi-constant-torque contractions. The contractions ranged between 50% maximum voluntary contractions (MVC) extension and 50% MVC flexion. Advanced EMG amplitude (EMG¦Ò) estimation processors were investigated, and three nonlinear EMG¦Ò-torque models were evaluated. Results show that advanced (i.e., whitened, multiple-channel) EMG¦Ò processors lead to improved joint torque estimation, compared to unwhitened, single-channel EMG¦Ò processors. Depending on the joint angle, use of the multiple-channel whitened EMG¦Ò processor with higher polynomial degrees produced a median error that was 50%-66% that found when using the single-channel, unwhitened EMG¦Ò processor with a polynomial degree of 1. The best angle-specific model achieved a minimum error of 3.39% MVCF90 (i.e., error referenced to MVC at 90¢X flexion), yet it does not allow interpolation across angles. The best model which parameterizes the angle dependence achieved an error of 3.55% MVCF90.
This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]. (2) EMG-force modeling of force produced at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (3) Identification of a nonlinear, dynamic EMG-torque relationship about the elbow was studied [WPI]. (4) Signal whitening preprocessing for improved classification accuracies in myoelectric control of a prosthesis was studied [with WPI and the University of New Brunswick].
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