Optimized Simulation of Upper Body Timing on the Production of Bat-Head Speed in Baseball Batting.

The objectives of this study were to (1) investigate the effect of the timing of the upper body joint motions in baseball batting on the bat-head speed and (2) develop and evaluate a simulation model inputting the individual hand forces on the bat. Twenty-three male collegiate baseball players performed tee batting set at waist height. A 10-segment angle-driven simulation model consisting of a bat and upper body was driven using the coordinate data of the standard motion. Performance optimization was conducted by changing the timing of the joint angle time histories of the upper body to increase the maximum bat-head speed. The optimization simultaneously estimated the individual hand forces by polynomial approximation dependent on the total bat forces to assess joint torques of the upper body. The bat-head speed increased to 39.2 m/s from 35.6 m/s, and the optimized timings were characterized by the earlier timing of the barrel-side elbow supination, wrist radial flexion, torso right lateral flexion, and the later timing of the barrel-side shoulder abduction. It is concluded that the skillful coordination of the individual joint movements for the upper body can produce a higher bat-head speed through effective sequencing of proximal to distal movements.

Influence of angular position on radar gun peak cricket ball speed measurements.

The purpose of this study was to determine the agreement in peak ball speed measured using a radar gun and motion capture system then examine the influence of angular position. Peak ball speed was recorded with the radar gun in-line with the ball trajectory (0° offset) and at 5° offsets up to 35°. Agreement between devices was calculated at 0° and for grouped offset bins (0-5°, 10-15°, 20-25°, and 30-35°). At 0°, a strong correlation (r = 0.99) and intraclass correlation coefficient (.984) were observed with a systematic overestimation by the radar gun (1.7 ± 0.8 m/s). The residual speed between devices at the 30-35° offset was significantly different to both 0-5° (p < .001) and 10-15° (p < .001) offsets. The radar gun consistently overestimated peak ball speed up to a ~20° offset and thus can be positioned out of the line-of-throw to avoid obstructions.

Optimisation of the upper body motion for production of the bat-head speed in baseball batting.

The purposes of this study were to 1) develop a simulation model of baseball batting utilising the standard motion, and 2) explore optimal motions of the upper body to increase the bat-head speed. Twenty-three male collegiate baseball players performed tee batting set at waist height. A ten-segment angle-driven simulation model consisting of a bat and upper body was driven using with the coordinate data of the standard motion. Performance optimisation was conducted to find joint angle time histories of the upper body that increase the maximum bat-head speed. In the evaluation of the simulation model, the root mean square error between the measured and simulation model was 0.19 m/s and 0.98° for the time histories of the bat-head speed and bat orientation angle. Performance optimisation was able to achieve a targeted increase in bat-head speed (35.6 m/s to 40.0 m/s) through greater barrel-side shoulder abduction, knob-side elbow flexion, and torso right lateral flexion around ball impact resulted in the bat accelerating in the hitting direction. It is concluded that the proposed simulation approach can be applied as a tool for further simulation analysis in various complex sporting motions.

A parametric feature extraction and classification strategy for brain-computer interfacing.

Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.