The Reactive Strength Index (RSI) is often used to quantify drop-jump (DJ) performance; however, not much is known about its biomechanical determinants. The purpose of this study was to investigate the correlations between the RSI and several biomechanical variables calculated from DJ performed with different initial drop heights. Twelve male NCAA Division I basketball players performed DJs from drop heights of 30, 45, and 60 cm. Force plates were used to calculate DJ performance parameters (ie, DJ height, contact time, and RSI) and DJ biomechanical variables (ie, vertical stiffness and eccentric/concentric energetics). Regression analyses were used to assess the correlations between variables at each drop height, and ANOVAs were used to assess the differences of all variables across drop heights. Follow-up analyses used 2 neural networks to determine if DJ performance and biomechanical data could accurately classify DJ trials by drop-height condition. Vertical-stiffness values were significantly correlated with RSI at each height but did not change across drop heights. Surprisingly, the RSI and other DJ parameters also did not vary across drop height, which resulted in the inability of these variables to accurately classify DJ trials. Given that vertical stiffness did not change across drop height and was highly correlated with RSI at each height, the RSI appears to reflect biomechanical behavior related to vertical stiffness during DJ. However, the inability of the RSI to accurately classify drop-height condition questions the use of RSI profiles established from DJs from different heights.

Ghtlifting performance. Joint kinematics and kinetics of the hip, knee, and ankle were calculated while 10 subjects performed a clean at 85% of 1 repetition maximum (1RM). Kinematic and kinetic time-series patterns were extracted with principal components analysis. Discrete scores for each time-series pattern were calculated and used to determine how each pattern was related to body mass–normalized 1RM. Two hip kinematic and 2 knee kinetic patterns were significantly correlated with relative 1RM. The kinematic patterns captured hip and trunk motions during the first pull and hip joint motion during the movement transition between the first and second pulls. The first kinetic pattern captured a peak in the knee extension moment during the second pull. The second kinetic pattern captured a spatiotemporal shift in the timing and amplitude of the peak knee extension moment. The kinematic results suggest that greater lift mass was associated with steady trunk position during the first pull and less hip extension motion during the second-knee bend transition. Further, the kinetic results suggest that greater lift mass was associated with a smaller knee extensor moments during the first pull, but greater knee extension moments during the second pull, and an earlier temporal transition between knee flexion-extension moments at the beginning of the second pull. Collectively, these results highlight the importance of controlled trunk and hip motions during the first pull and rapid employment of the knee extensor muscles during the second pull in relation to weightlifting performance....

BACKGROUND: Advances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the u