It is known that the springlike properties of muscles provide automatic load compensation during weight bearing. How crucial is sensory control of the motor output given these basic properties of the locomotor system? To address this question, a neuromuscular model was used to test two hypotheses. (1) Stretch reflexes are too weak and too delayed to contribute significantly to weight-bearing. (2) The important contributions of sensory input involve state-dependent processing. We constructed a two-legged planar locomotor model with 9 segments, driven by 12 musculotendon actuators with Hill-type force-velocity and monotonic force-length properties. Electromyographic (EMG) profiles of the simulated muscle groups during slow level walking served as actuator activation functions. Spindle Ia and tendon organ Ib sensory inputs were represented by transfer functions with a latency of 35 ms, contributing 30% to the net EMG profile and gated to be active only when the receptor-bearing muscles were contracting. Locomotor stability was assessed by parametric variations of actuator maximum forces during locomotion in open-loop ("deafferented") trials and in trials with feedback control based on either sensory-evoked stretch reflexes or finite-state rules. We arrived at the following conclusions. (1) In the absence of sensory control, the intrinsic stiffness of limb muscles driven by a stereotyped rhythmical pattern can produce surprisingly stable gait. (2) When the level of central activity is low, the contribution of stretch reflexes to load compensation can be crucial. However, when central activity provides adequate load compensation, the contribution of stretch reflexes is less significant. (3) Finite-state control can greatly extend the adaptive capability of the locomotor system.

Online control of movement requires complex integration of predictive central feedforward and peripheral sensory feedback signals. We studied the hand trajectories of human subjects pointing to visual targets that abruptly changed locations by different amounts and modeled the mechanism of rapid online correction using a dynamic model of a two-joint limb. Small unperceived and large detected target displacements could be attributed to different origins (motor execution errors vs. environmental changes, respectively) and compensated differently. However, the behavioral findings indicate that the rapid feedback pathway is recruited regardless of the amplitude or subjective awareness of target displacement and that the size of the earliest correction is always proportional to the amplitude of the target displacement over the tested range of perturbations. The modeling findings suggest that the rapid online corrections can be accomplished by superimposing a dynamically appropriate error correction signal onto the outgoing feedforward motor command to the original target. Furthermore, the modeling shows that the online correction mechanism must include compensation for the dynamic mechanical properties of the limb and for sensory delays in its error-correction pathway.

Psychophysical studies have reported an overestimation of limb position in the direction of movement during the early part of active movements. The main hypothesis tested in this study is that the overestimation results from a process of forward prediction of limb state driven by an efference copy of the outgoing motor command. This hypothesis predicts that position overestimation should decrease or disappear during passive movements, for which there should be no efference copy. Seven subjects were asked to remember and to report the perceived angle of their elbow joint at different times during active and passive movements. They showed a highly velocity-dependent overestimation of the elbow joint angle near the beginning of the movement in both active and passive trials. Toward the end of the movement, subjects showed a relatively velocity-independent underestimation of their elbow angle in all trials. Contrary to the prediction of the efference copy hypothesis, the amplitude and the velocity-dependent slope of the elbow angle overestimation were both greater during the early part of passive movements than active movements. This indicates that psychophysical evidence of early overestimation of arm position on its own is not a sufficient proof of forward prediction based on an efference copy, at least under the conditions of this study. Decreased errors during active movements suggest that an efference copy can improve the accuracy of state estimation during active movements. Error patterns seem to parallel the likely level of sensorimotor noise, suggesting a probabilistic mechanism for position estimation.

Current diagnosis and treatment of movement impairment post-stroke is based on the subjective assessment of select movements by a trained clinical specialist. However, modern low-cost motion capture technology allows for the development of automated quantitative assessment of motor impairment. Such outcome measures are crucial for advancing post-stroke treatment methods. We sought to develop an automated method of measuring the quality of movement in clinically-relevant terms from low-cost motion capture. Unconstrained movements of upper extremity were performed by people with chronic hemiparesis and recorded by standard and low-cost motion capture systems. Quantitative scores derived from motion capture were compared to qualitative clinical scores produced by trained human raters. A strong linear relationship was found between qualitative scores and quantitative scores derived from both standard and low-cost motion capture. Performance of the automated scoring algorithm was matched by averaged qualitative scores of three human raters. We conclude that low-cost motion capture combined with an automated scoring algorithm is a feasible method to assess objectively upper-arm impairment post stroke. The application of this technology may not only reduce the cost of assessment of post-stroke movement impairment, but also promote the acceptance of objective impairment measures into routine medical practice.

Neural control of movement can only be realized though the interaction between the mechanical properties of the limb and the environment. Thus, a fundamental question is whether anatomy has evolved to simplify neural control by shaping these interactions in a beneficial way. This inductive data-driven study analyzed the patterns of muscle actions across multiple joints using the musculoskeletal model of the human upper limb. This model was used to calculate muscle lengths across the full range of motion of the arm and examined the correlations between these values between all pairs of muscles. Musculoskeletal coupling was quantified using hierarchical clustering analysis. Muscle lengths between multiple pairs of muscles across multiple postures were highly correlated. These correlations broadly formed two proximal and distal groups, where proximal muscles of the arm were correlated with each other and distal muscles of the arm and hand were correlated with each other, but not between groups. Using hierarchical clustering, between 11 and 14 reliable muscle groups were identified. This shows that musculoskeletal anatomy does indeed shape the mechanical interactions by grouping muscles into functional clusters that generally match the functional repertoire of the human arm. Together, these results support the idea that the structure of the musculoskeletal system is tuned to solve movement complexity problem by reducing the dimensionality of available solutions.