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Biomedical subjects

A T Bahill

Publications and source records attributed to A T Bahill.

24 records · Page 2Linked to original sources
PubMed1975

Computer simulation of overshoot in saccadic eye movements.

The human horizontal eye movement system produces quick, precise, conjugate eye movements called saccades. These are important in normal vision. For example, reading tasks exclusively utilize saccadic eye movements. The majority of saccades have dynamic overshoot. The amplitude of this overshoot is independent of saccadic amplitude, and is such that it places the image of the stimulus within the retinal region of maximum acuity within a minimum of time. A computer based model of the saccadic mechanisms was used to study the origin of this overshoot. It was discussed that dynamic overshoot cannot be attributed to biomechanism properites of the eye movement mechanism, but must instead be explained by variations in the controlling nervous activity. The form of this neural controller signal is very similar to that required for a time optimal response of an inertial system.

Computers↗
PubMed1975

Closely spaced saccades.

The relationships between saccadic velocity, duration, and magnitude have been used to prove the normalcy of saccades with intersaccadic intervals of less than 200 ms. Pairs of normal saccades with small intersaccadic intervals will have the second saccade larger or smaller and going in the same or the opposite direction than the first saccade. These normal saccades may be horizontal, vertical, or oblique.

Electrooculography↗
PubMedDate not supplied

Two methods for recommending bat weights.

Baseball players swung very light and very heavy bats through our instrument and the speed of the bat was recorded. These data were used to make mathematical models for each person. Then these models were coupled with equations of physics for bat-ball collisions to compute the Ideal Bat Weight for each individual. However, these calculations required the use of a sophisticated instrument that is not conveniently available to most people. So, we tried to find items in our database that correlated with Ideal Bat Weight. However, because many cells in the database were empty, we could not use traditional statistical techniques or even neural networks. Therefore, three new methods were used to estimate the missing data: (i) a neural network was trained using subjects that had no empty cells, then that neural network was used to predict the missing data, (ii) the data patching facility of a commercial software package was used, and (iii) the empty cells were filled with random numbers. Then, using these fully populated databases, several simple models were derived for recommending bat weights.

Adolescent↗