A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings

The nervous system controls the behavior of complex kinematically redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we propose a model based on the assumption that the nervous system: 1) processes static (e.g., gravitational) and dynamic (e.g., inertial) forces separately; 2) calculates appropriate dynamic controls to master the dynamic forces and progress toward the goal according to principles of optimal feedback control; 3) uses the size of the dynamic commands (effort) as an optimality criterion; and 4) can specify movement duration from a given level of effort. The model was used to control kinematic chains with 2, 4, and 7 degrees of freedom [planar shoulder/elbow, three-dimensional (3D) shoulder/elbow, 3D shoulder/elbow/wrist] actuated by pairs of antagonist muscles. The muscles were modeled as second-order nonlinear filters and received the dynamics commands as inputs. Simulations showed that the model can quantitatively reproduce characteristic features of pointing and grasping movements in 3D space, i.e., trajectory, velocity profile, and final posture. Furthermore, it accounted for amplitude/duration scaling and kinematic invariance for distance and load. These results suggest that motor control could be explained in terms of a limited set of computational principles.

Surface electromyography (sEMG) is an important tool to estimate muscular activity at work. There is, however, a great inter-individual variation, even in carefully standardized work tasks. The sEMG signal is attenuated in the subcutaneous tissues, differently for each subject, which requires normalization. This is commonly made in relation to a reference contraction, which by itself, however, introduces a variance. A normalization method that is independent of individual motivation, motor control and pain inhibition would be desirable. The aim of the study was to explore the influence of the subcutaneous tissue thickness on sEMG amplitude. Ultrasound measurements of the muscle to skin surface distance were made bilaterally over the trapezius muscle in 12 females. Skinfold caliper measurements from these sites, as well as from four other sites, were made, body mass index (BMI) was recorded, and sEMG was recorded at maximal and submaximal contractions. The muscle-electrode distance, as measured by ultrasound, explained 33% and 31% (on the dominant and non-dominant sides respectively) of the variance of the sEMG activity at a standardized submaximal contraction (average between the sides, 46%); for maximal contractions the explained variance was 21%. Trapezius skinfold measurements showed poor correlations with sEMG. Instead, the mean of skinfold measurements from other sites explained as much as 68% (submaximal contraction). The corresponding figure for BMI was 67%. In conclusion, skinfold thickness explains a major part of the inter-individual variance in sEMG amplitude, and normalization to this measure is a possibility worth further evaluation.