No abstract

1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment < 50% maximum excitation) and broad recruitment range conditions (limit of recruitment > 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)

Our goal was to provide some insights into how the CNS controls and maintains an upright standing posture, which is an integral part of activities of daily living. Although researchers have used simple performance measures of maintenance of this posture quite effectively in clinical decision making, the mechanisms and control principles involved have not been clear. We propose a relatively simple control scheme for regulation of upright posture that provides almost instantaneous corrective response and reduces the operating demands on the CNS. The analytic model is derived and experimentally validated. A stiffness model was developed for quiet standing. The model assumes that muscles act as springs to cause the center-of-pressure (COP) to move in phase with the center-of-mass (COM) as the body sways about some desired position. In the sagittal plane this stiffness control exists at the ankle plantarflexors, in the frontal plane by the hip abductors/adductors. On the basis of observations that the COP-COM error signal continuously oscillates, it is evident that the inverted pendulum model is severely underdamped, approaching the undamped condition. The spectrum of this error signal is seen to match that of a tuned mass, spring, damper system, and a curve fit of this "tuned circuit" yields omega n the undamped natural frequency of the system. The effective stiffness of the system, Ke, is then estimated from Ke = I omega n2, and the damping B is estimated from B = BW X I, where BW is the bandwidth of the tuned response (in rad/s), and I is the moment of inertia of the body about the ankle joint. Ten adult subjects were assessed while standing quietly at three stance widths: 50% hip-to-hip distance, 100 and 150%. Subjects stood for 2 min in each position with eyes open; the 100% stance width was repeated with eyes closed. In all trials and in both planes, the COP oscillated virtually in phase (within 6 ms) with COM, which was predicted by a simple 0th order spring model. Sway amplitude decreased as stance width increased, and Ke increased with stance width. A stiffness model would predict sway to vary as Ke-0.5. The experimental results were close to this prediction: sway was proportional to Ke(-0.55). Reactive control of balance was not evident for several reasons. The visual system does not appear to contribute because no significant difference between eyes open and eyes closed results was found at 100% stance width. Vestibular (otolith) and joint proprioceptive reactive control were discounted because the necessary head accelerations, joint displacements, and velocities were well below reported thresholds. Besides, any reactive control would predict that COP would considerably lag (150-250 ms) behind the COM. Because the average COP was only 4 ms delayed behind the COM, reactive control was not evident; this small delay was accounted for by the damping in the tuned mechanical system.

1. Control of posture in quiet stance has been quantified by center of pressure (COP) changes in the anterior-posterior (A/P) and medial-lateral (M/L) directions from a single force platform. Recording from a single force platform, researchers are unable to recognize two separate mechanisms that become evident when two force platforms are used. Depending on the stance position taken, many combinations of an ankle mechanism and a hip (load/unload) mechanism are evident. In side-by-side stance, A/P balance is totally under ankle (plantar/dorsiflexor) control, whereas M/L balance is under hip (abductor/adductor) control. In tandem stance, the A/P balance is dominated by the hip mechanism, with mixed and small or sometimes negligible contributions by the ankle plantar/dorsiflexors: for M/L balance, the reverse is evident; ankle invertors/evertors dominate, with mixed and small contribution from the hip load/unload mechanism. In an intermediate 45 degrees stance position, both ankle and hip mechanisms contribute to the net balance control in totally different ways. In the M/L direction the two strategies reinforce, whereas in the A/P direction the ankle mechanism must overcome and cancel most of the inappropriate contribution by the hip load/unload mechanism. A spatial plot of the separate mechanisms reveals the fact that the random-looking COP scatter plot is nothing more than a spatial and temporal summation of two separate spatial plots. A straight line joining the individual COPs under each foot is the load/unload line controlled by the hip mechanism. At right angles to this load/unload line in the side-by-side and tandem positions is the independent control line by the ankle muscles. In an intermediate standing position, the separate control lines exist, but now the ankle control is not orthogonal to the load/unload line; rather, it acts at an angle of approximately 60 degrees. The direction of these ankle control and load/unload lines also allows us to pinpoint the muscle groups responsible at the ankle and hip in any of the stance positions.

A descriptive study of the biomechanical variables of the walking patterns of the fit and healthy elderly compared with those of young adults revealed several significant differences. The walking patterns of 15 elderly subjects, selected for their active life style and screened for any gait- or balance-related pathological conditions, were analyzed. Kinematic and kinetic data for a minimum of 10 repeat walking trials were collected using a video digitizing system and a force platform. Basic kinematic analyses and an inverse dynamics model yielded data based on the following variables: temporal and cadence measures, heal and toe trajectories, joint kinematics, joint moments of force, and joint mechanical power generation and absorption. Significant differences between these elderly subjects and a database of young adults revealed the following: the same cadence but a shorter step length, an increased double-support stance period, decreased push-off power, a more flat-footed landing, and a reduction in their "index of dynamic balance." All of these differences, except reduction in index of dynamic balance, indicate adaptation by the elderly toward a safer, more stable gait pattern. The reduction in index of dynamic balance suggests deterioration in the efficiency of the balance control system during gait. Because of these significant differences attributable to age alone, it is apparent that a separate gait database is needed in order to pinpoint falling disorders of the elderly.