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No description available.

The ulnar collateral ligament (UCL) is the primary restraint against valgus forces at the elbow. This structure cannot support the entire load placed upon the medial elbow during overhand throwing motions such as pitching a baseball. In this study we measured the contributions that different muscle groups make to the stability of the medial elbow, under conditions intended to reproduce the loads during pitching by varying the forearm position and loading conditions. We also evaluated the strength of the elbow musculature for the possibility of a training effect in the dominant arm of 11 male high school baseball pitchers. We collected surface EMG data in the two forearm positions to determine if the different positions used in various pitches have an effect on muscular action. We also tested an isometric and dissipative loading condition to determine if the muscles activity was load reactive. The four muscle groups tested were the flexor-pronator group (FP), the extensor-supinator group (ES), the tricep brachii (TB), and the pectoralis major (PM). We found significantly (p-value=0.001) higher peak activity levels of the flexor-pronator group in the neutral forearm position (79.4% MVIC �� 27.0% MVIC full trial peak, 30.8% MVIC �� 20.8% MVIC initial l50ms peak following activation) when compared to the supinated position (55.5% MVIC �� 29.6% MVIC full trial peak, 16.9% MVIC �� 14.8% MVIC initial l50ms peak following activation), which may explain the link between breaking pitches and medial elbow injury. We found an increase (p-value=0.001) in force output of the dominant arm (49.3 N/kg �� 12.5 N/kg) over the non-dominant arm (38.1 N/kg �� 11.0 N/kg). This finding is attributed to a training effect, which assists in protecting the elbow. These findings help provide baseline muscle activity information on protection of the medial soft tissue structures of the elbow. / Graduation date: 2004

A computer model simulating the electrical activity of muscles of the upper arm during elbow motion is presented. The output of the model is an Electromyographic (EMG) signal. System identification is performed on the EMG signals using autoregressive moving average (ARMA) modelling. The calculated ARMA coefficients are then used as the feature set for pattern recognition. Pattern recognition is performed on the EMG signals to attempt to identify which of four possible motions is producing the signal. The results of pattern recognition are compared with results from pattern recognition of real EMG signals. The model is shown to be useful in predicting general trends found in the real data, but is not robust enough to predict accurate quantitative results. Simplifying assumptions about the filtering effects of body tissue, and about the size and position of muscles, are conjectured to be the most likely reasons the model is not quantitatively accurate. / Graduation date: 1992

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