The activity patterns of trunk muscles are commonly neglected, in spite of their importance for maintaining body shape. Analysis of the biomechanics of the trunk under static conditions has led to predictions of the activity patterns. These hypotheses are tested experimentally by surface electromyography (EMG). Five horses, with and without a rider, were examined in the walk, trot and canter. Footfall was synchronised with EMG by an accelerometer. Averages of ten consecutive cycles were calculated and compared by statistical methods. The start and stop times of the muscle activities of 5-10 undisturbed EMG plots were determined and the averages and standard deviations calculated. In walking, muscle activities are minor. Electromyography (EMG) activity was increased in the m. rectus during the three-limb support. When the bending moments assume their greatest values, for example while the horses' mass is accelerated upward (two times earth acceleration) in the diagonal support phases in trot and canter the m. rectus, connecting the sternum with the pubic bone is most active. The m. obl. externus is most active when the torsional and bending moments are greatest during the same support phases, but not bilaterally, because the forces exerted on one side by the (recorded) m. obl. externus are transmitted on the other side by the (not recorded) m. obl. internus. While the hindlegs touch the ground in the trot and canter, ground reaction forces tend to flex the hip joint and the lumbar spine. Therefore, the vertebral column needs to be stabilised by the ipsilateral m. longissimus dorsi, which in fact can be observed. As a whole, our EMG data confirm exactly what has been predicted by theoretical analysis.

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Spinal pattern generators (SPGs), which are neural networks without a central input from the brain may be responsible for controlling locomotion. In this study, we used neural oscillators to examine the rhythmic patterns generated at the ankle during walking. Seven healthy male subjects were requested to walk at their normal self-selected speed on a treadmill. Force measurements acquired from pressure insoles, electromyography and kinematic data were captured simultaneously. The SPG model consisted of a simple oscillator made up of two neurons; one neuron will activate an ankle extensor and the other will activate an ankle flexor. The outputs of the oscillator represented the muscle activation of each muscle. A nonlinear least squares algorithm was used to determine a set of parameters that would optimise the differences between model output and experimental data. Insole forces and hip angles of six consecutive strides were used as inputs to the model, which generated outputs that closely fitted experimental data. Our results showed that it is possible to reproduce muscle activations using neural oscillators. A close correlation between simulated and measured muscle activations indicated that spinal control should not be underestimated in models of human locomotion.

BackgroundSpinal pattern generators (SPG) are neural networks in the spinal cord that do not require a central input from the brain to generate a motor output. We wanted to determine whether SPG can adapt to the changing motor demands from walking at different speeds, and performing silly walks.MethodsAn SPG model consisting of an oscillator made up of two neurons was utilised in this study; one neuron activates the soleus and the other activates the tibialis anterior. The outputs of the SPG model therefore represent the electromyographic measurements from each muscle. Seven healthy subjects were requested to perform silly walks, normal walking at self-selected speed (4.8 ± 0.5 km/h), 3.5 km/h, 4.0 km/h and 4.5 km/h on a treadmill. Loading and hip angles were used as inputs into the model.ResultsNo significant differences in the model parameters were found between normal walking at self-selected speed and other walking speeds. Only the adaptation time constant for the ankle flexor during silly walks was significantly different from the other normal walking trials.ConclusionWe showed that SPG in the spinal cord can interpret and respond accordingly to velocity-dependent afferent information. Changes in walking speed do not require a different motor control mechanism provided there is no disruption to the alternating muscular activations generated at the ankle.

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