VALIDATION OF A 3-DIMENSIONAL VIDEO MOTION CAPTURE SYSTEM FOR DETERMINING BARBELL POWER AND VELOCITY DURING THE BENCH PRESS

Andrew C. Fry, Luke Bradford, Trent Herda, Joseph Weir FACSM, Michael Lane, Matthew Andre, Andrea Hudy, J. Deckert and J. Siedlik.Neuromechanics Laboratory and Kansas Athletics Inc., University of Kansas, Lawrence, KS Analyses of barbell kinetics and kinematics have typically required the use of force plates, tether-based position transducers, or digitized video analysis. PURPOSE: To determine the validity of a 3-dimensional video markerless motion capture system for determining barbell kinetics and kinematics. METHODS: Two 3-D video cameras sampling at 30 Hz and mounted on the top of a power rack were interfaced with a self-contained computer and software system, and operated with a touch screen (EliteForm, Lincoln, NE). For laboratory comparison purposes, a ceiling–mounted linear position transducer (Unimeasure, Corvallis, OR) was attached via a tether to the barbell. Data from the position transducer was sampled at 1000 Hz using a BioPac data acquisition system (Goleta, CA). Velocity (m.s-1) and power (W) were derived using LabView software (National Instruments, Austin, TX). One weight-trained male subject (age = 25 yrs, hgt = 1.75 m, BW = 82.6 kg, 1 RM = 161.0 kg) performed the barbell bench press exercise for 10 sets x 1 repetition at 30, 40, 50, 60, 70 and 80% 1 RM loads using maximal acceleration during the concentric phase. Dependent variables included peak (PV) and X̅ velocity (MV) and peak (PP) and X̅ power (MP). Linear regressions between lab-derived and 3-D video-derived data provided correlation coefficients, and regression slopes (b). Bland-Altman plots were used to determine X̅ differences, from which effect sizes (Cohen’s D) and % error for the 3-D camera system was determined. RESULTS: Lab-derived mean values for all loads ranged as follows; MV = 0.36 – 1.00 m.s-1, PV = 0.47 – 1.60 m.s-1, MP = 460.9 – 621.6 W, and PP = 619.9 – 1055.6 W. CONCLUSION: The 3-D video markerless motion capture system provided accurate and valid barbell velocity and power data for the bench press exercise. Supported in part by Nebraska Global LL

Turbulent separated shear flow control by surface plasma actuator: experimental optimization by genetic algorithm approach

The final publication is available at Springer via http://dx.doi.org/10.1007/s00348-015-2107-3The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U0 = 15 m/s, Reh = 30,000, Re¿ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.Peer ReviewedPostprint (author's final draft