A fusion integration algorithm is used to estimate the one-dimensional center of mass (COM) trajectory from force platform data. The resulting COM trajectory combines the best attributes of several established algorithms used to estimate the COM trajectory, and it appears to have the advantage of being robust, accurate, continuous in its higher derivatives, and fast to obtain. In current research projects, variations of the fusion integration algorithm have been adapted by the authors for the analysis of postural balance and the sensing of limb orientations with inertial measurement units.
Matthew Brodie, Alan Walmsley and Wyatt Page
Marko T. Korhonen, Harri Suominen, Jukka T. Viitasalo, Tuomas Liikavainio, Markku Alen and Antti A. Mero
Eighteen young (23 ± 4 yr) and 25 older (70 ± 4 yr) male sprinters were examined for ground reaction force (GRF) and temporal-spatial variables. The data were collected during maximum-speed phase, and variability and symmetry indices were calculated from a total of 8 steps. There was little variation (CV < 6%) in vertical and resultant GRF and kinematic variables, while impact loading had high variability (CV: 10–21%). Overall, the pattern of variability was similar in both groups. Yet, a small but significant age-related increase in CV was evident in horizontal GRFs. There was a variable-specific asymmetry between legs but it was not related to leg dominance. No age differences existed in the symmetry indices. Results indicate that only selected force platform variables are symmetric and repeatable enough so that their use for comparison purposes is appropriate. Data also suggest that aging may increase variability in certain biomechanical measures, whereas symmetry is not affected by age.
Juan V. Durá, Juan M. Belda, Rakel Poveda, Álvaro Page, José Laparra, José Das, Jaime Prat and Ana C. García
The effect of walking velocity on force platform measures is examined by means of functional regression and nonfunctional regression analyses. The two techniques are compared using a data set of ground reaction forces. Functional data analysis avoids the need to identify significant points, and provides more information along the waveform.
Mary Emily Littrell, Young-Hui Chang and Brian P. Selgrade
Multicamera gait analysis systems and force platforms are used to measure the kinematics and kinetics of locomotion. 1 – 5 Although 3-dimensional analysis provides valuable insights, these systems are difficult to transport and prohibitively expensive for widespread clinical use. However, the
Katherine L. Hsieh, Yaejin Moon, Vignesh Ramkrishnan, Rama Ratnam and Jacob J. Sosnoff
also been linked to a greater fall risk. 13 , 14 In these studies, VTC was determined based on COP derived from force platforms or COM derived from high-speed motion capture cameras. Although these techniques of measuring VTC are generally considered the gold standard for postural stability, they are
Patrice R. Rougier and Samir Boudrahem
Past studies have emphasized the beneficial effect of additional visual feedback (VFB) on the capacity of healthy adults to decrease the amplitudes of the center-of-pressure minus center-of-gravity (CP-CGv) movements. To better assess these capacities, 56 subjects were asked to stand still on a force platform and to use the visual information provided. Dependency coefficients, based on their capacity to lower their CP-CGv movements and therefore relax their lower limb muscles, as well as parameters aimed at characterizing their postural strategies were measured across VFB conditions including (1) CP displacements in real time (VFBCP0), (2) CP displacements with a 600-ms delay (VFBCP600), and (3) CP-CGv displacements with a 600-ms delay (VFBCP-CG600). A non-VFB condition (eyes open) was also included. Several linear correlations were used to specify the relation between subjects’ capacity to relax, compared with the VFBCP0 condition, across the three remaining conditions. The data highlight the complementary nature of the VFB conditions and establish the postural control behaviors necessary to use these VFB protocols efficiently.
Gavin L. Moir, Alberto Garcia and Gregory B. Dwyer
To investigate the intersession reliability of selected kinematic and kinetic variables during countermovement vertical jumps (CMJs).
Thirty-five men and 35 women performed CMJs on a force platform during four testing sessions each separated by 1 wk. Kinematic variables included time in the air (TIA), take-off velocity (TOV), total vertical displacement of the center of mass (TJH). and countermovement depth, whereas kinetic variables included positive impulse, negative impulse, vertical stiffness, and rates of force development. Systematic bias was assessed by calculating the 90% confidence interval of the change in the mean between consecutive testing sessions and between the first and final testing session for each variable. Coefficients of variation (CV) and intraclass correlation coefficients (ICC) were also calculated.
Systematic bias was observed only for peak rate of force development during the concentric phase of the movement. For TIA, TOV, and TJH, CV values ranged from 1.7% to 6.6%, with ICC values ranging from 0.82 to 0.97. The other variables showed greater variation (CV range: 1.7% to 39.9%; ICC range: 0.04 to 0.99). Only slight gender differences were found in the reliability statistics, and the reliability of most of the variables was diminished as the time between the testing sessions was increased.
Even though practitioners can expect good reliability for jump height measured from a force platform in men and women, other kinematic and kinetic variables often assessed during vertical jumps may not be reliable.
Brian A. Blanksby, Jennifer R. Simpson, Bruce C. Elliott and Keith McElroy
Because turning can account for one-third of breaststroke race time in 25 m pools, it is possible that enhancing turning techniques can improve performance significantly. Underwater video cameras and a force platform were used to analyze turning techniques of 23 age-group breaststrokers during three 50 m push-start, maximum-effort swims. The criterion measure was the time elapsed between passing the 5 m mark on the approach and departure from the wall (5 m round-trip time [RTT]). Correlations revealed significant commonality of variance (p < .01) between the 5 m RTT and the 2.5 m RTT, 50 m time, average single-stroke velocity, peak reaction force, pivot time, impulse, peak horizontal velocity off the wall, arm and leg split-stroke resumption distances, surfacing distance, surfacing time, and horizontal velocity, height, and mass of the subjects. All swimmers achieved a net gain at the turn in that the mean 5 m RTT (20% of the distance) represented 18.26% of the total swimming time. Following stepwise regression, a successful turn was predicted by the equation 17.113 - 0.322 surfacing distance - 0.036 height - 0.723 surfacing horizontal velocity + 0.723 pivot time - 0.65 peak horizontal velocity.
Liam P. Kilduff, Huw Bevan, Nick Owen, Mike I.C. Kingsley, Paul Bunce, Mark Bennett and Dan Cunningham
The ability to develop high levels of muscle power is considered an essential component of success in many sporting activities; however, the optimal load for the development of peak power during training remains controversial. The aim of the present study was to determine the optimal load required to observe peak power output (PPO) during the hang power clean in professional rugby players.
Twelve professional rugby players performed hang power cleans on a portable force platform at loads of 30%, 40%, 50%, 60%, 70%, 80%, and 90% of their predetermined 1-repetition maximum (1-RM) in a randomized and balanced order.
Relative load had a significant effect on power output, with peak values being obtained at 80% of the subjects’ 1-RM (4466 ± 477 W; P < .001). There was no significant difference, however, between the power outputs at 50%, 60%, 70%, or 90% 1-RM compared with 80% 1-RM. Peak force was produced at 90% 1-RM with relative load having a significant effect on this variable; however, relative load had no effect on peak rate of force development or velocity during the hang power clean.
The authors conclude that relative load has a significant effect on PPO during the hang power clean: Although PPO was obtained at 80% 1-RM, there was no significant difference between the loads ranging from 40% to 90% 1-RM. Individual determination of the optimal load for PPO is necessary in order to enhance individual training effects.
Aaron T. Scanlan, Jordan L. Fox, Nattai R. Borges and Vincent J. Dalbo
Declines in high-intensity activity during game play (in-game approach) and performance tests measured pre- and postgame (across-game approach) have been used to assess player fatigue in basketball. However, a direct comparison of these approaches is not available. Consequently, this study examined the commonality between in- and across-game jump fatigue during simulated basketball game play.
Australian, state-level, junior male basketball players (n = 10; 16.6 ± 1.1 y, 182.4 ± 4.3 cm, 68.3 ± 10.2 kg) completed 4 × 10-min standardized quarters of simulated basketball game play. In-game jump height during game play was measured using video analysis, while across-game jump height was determined pre-, mid-, and postgame play using an in-ground force platform. Jump height was determined using the flight-time method, with jump decrement calculated for each approach across the first half, second half, and entire game.
A greater jump decrement was apparent for the in-game approach than for the across-game approach in the first half (37.1% ± 11.6% vs 1.7% ± 6.2%; P = .005; d = 3.81, large), while nonsignificant, large differences were evident between approaches in the second half (d = 1.14) and entire game (d = 1.83). Nonsignificant associations were evident between in-game and across-game jump decrement, with shared variances of 3–26%.
Large differences and a low commonality were observed between in- and across-game jump fatigue during basketball game play, suggesting that these approaches measure different constructs. Based on our findings, it is not recommended that basketball coaches use these approaches interchangeably to monitor player fatigue across the season.