The electromyogram (EMG) provides a measure of a muscle's involvement in the execution of a motor task. Successful completion of an activity, such as walking, depends on the efficient motor control of a group of muscles. In this paper, we present a method to quantify the intricate phasing and activation levels of a group of muscles during gait. At the core of our method is a multidimensional representation of the EMG activity observed during a single stride. This representation is referred to as a "trajectory." A hierarchical clustering procedure is used to identify representative classes of muscle activity patterns. The relative frequencies with which these motor patterns occur during a session (i.e., a series of consecutive strides) are expressed as histograms. Changes in walking strategy will be reflected as changes in the relative frequency with which specific gait patterns occur. This method was evaluated using EMG data obtained during walking on a level and a moderately-inclined treadmill. It was found that the histogram changes due to artificially altered gait are significantly larger than the changes due to normal day-to-day variability.

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

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Lunar communications can be problematic for mission success when trading multiple objectives such as reduced mass, landing trajectory, and the need for communication with other orbiting vehicles and ground stations. To this end, reusability of existing vehicle components─for purposes other than the original intent─is an enticing option. In the case of Constellation, the Ares upper stage instrument unit avionics IUA has promise for re-use as a lunar relay satellite. The guidance, navigation and control GNC avionics along with the electrical power system can be re-used to position the IUA into a suitable lunar orbit for use as a relay. The IUA can be inserted into a medium lunar orbit via re-use of the earth departure stage EDS for the lunar orbit insertion LOI burn. Additionally, the descent module from the Altair can be re-used as either a beacon on the lunar surface or as a relay node for surface operations. The ascent module can be reconfigured and reused as an orbiting relay satellite. The re-use of these modules would greatly improve the effective delivered mass. This specific instance of re-use improves the following mission objectives a) enhanced safety via improved situational awareness, b) increased public visibility of NASA's lunar mission, and c) higher volume of data for scientific discovery. We present an architecture for re-use of modules to populate a lunar communications satellite system. 12