Propulsive forces were assessed using a hand differential pressure system (Aquanex System, Swimming Technology Research, Richmond, VA, USA) with a 0.2% measurement error [21 ]. The system is composed of two pressure sensors (type A, Swimming Technology Research, Richmond, VA, USA) that were positioned between the third and fourth metacarpals to measure the pressure between the palmar and dorsal surfaces of both hands. They allowed assessing the peak force of dominant (PFD) and nondominant (PFND) upper limbs in Newton (N). A signal-processor (AcqKnowledge v.3.7.3, Biopac Systems, Santa Barbara, CA, USA) was used to export data with a 5 Hz cutoff low-pass 4th order Butterworth filter upon residual analysis. The first positive and negative peaks (one cycle) were discarded, being considered the subsequent 5 cycles. The higher value (positive) was retrieved for further analysis. Symmetry index (SI, %), as a coordination measure, was estimated as proposed by Robinson et al. [22 ].
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11 protocols using acqknowledge v 3
1
Measuring Upper Limb Propulsive Forces
2
Propulsive Force Symmetry Assessment
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Propulsive forces were assessed by a hydrodynamic measurement system previously validated [7 ] with 0.2% of measurement error. The system is composed of two independent sensors that are positioned between the phalanges of the middle and ring fingers of both hands and allow assess to peak force of dominant (PropulsiveFD) and nondominant (PropulsiveFND) upper limbs in Newton (N). A signal-processor (AcqKnowledge v.3.7.3, Biopac Systems, Santa Barbara, CA, USA) was used to export data with a 5 Hz cutoff low-pass 4th order Butterworth filter upon residual analysis. The first positive and negative peak (one cycle) were discarded. Symmetric Index (SI, %) was estimated as proposed by Robinson, Herzog, and Nigg [12 ]:
where represents the force produced by the dominant upper limb, and represents the force produced by the nondominant upper limb.
Symmetry data was interpreted as suggested by the same authors, where if SI = 0%, there was perfect symmetry; if 0% ˃ SI < 10%, there was symmetric motion; and if SI ≥ 10%, there was asymmetric motion. The rate of force production for the dominant upper limb (RateFD) was considered as follows: (100 × PropulsiveFD)/(IsometricFD).
where represents the force produced by the dominant upper limb, and represents the force produced by the nondominant upper limb.
Symmetry data was interpreted as suggested by the same authors, where if SI = 0%, there was perfect symmetry; if 0% ˃ SI < 10%, there was symmetric motion; and if SI ≥ 10%, there was asymmetric motion. The rate of force production for the dominant upper limb (RateFD) was considered as follows: (100 × PropulsiveFD)/(IsometricFD).
3
Biomechanics of Freestyle Swimming
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SL and SF were selected to monitor the stroke biomechanics. Before the data collection, the swimmers performed a warm-up session. Each swimmer performed three all-out bouts of 25 m in freestyle swimming with a push-off start. The swimmers had a 30minute rest between trials ensuring a full recovery. The average value of the three trials was used for further analysis.
A speedometer cable (Swim speedometer, Swimsportec, Hildesheim, Germany) was attached to the swimmer's hip. A 12-bit resolution acquisition card (USB-6008, National Instruments, Austin, TX, USA) was used to transfer data ( f = 50 Hz) from the speedometer to a software interface in LabVIEW ® (v.2009) (Barbosa et al., 2015) . Data was exported to a signal processing software (AcqKnowledge v.3.5, Biopac Systems, Santa Barbara, CA, USA) and filtered with a 5 Hz cut-off low-pass fourthorder Butterworth filter. The swimming velocity was computed in the middle 15 m as v = d t -1 . Afterwards, the SF (in cycles•min -1 , and next converted to Hz) was measured with a stroke counter (base 3) by two expert evaluators (ICC = 0.97). The SL was calculated as SL = v•SF -1 (Craig & Pendergast, 1979) .
A speedometer cable (Swim speedometer, Swimsportec, Hildesheim, Germany) was attached to the swimmer's hip. A 12-bit resolution acquisition card (USB-6008, National Instruments, Austin, TX, USA) was used to transfer data ( f = 50 Hz) from the speedometer to a software interface in LabVIEW ® (v.2009) (Barbosa et al., 2015) . Data was exported to a signal processing software (AcqKnowledge v.3.5, Biopac Systems, Santa Barbara, CA, USA) and filtered with a 5 Hz cut-off low-pass fourthorder Butterworth filter. The swimming velocity was computed in the middle 15 m as v = d t -1 . Afterwards, the SF (in cycles•min -1 , and next converted to Hz) was measured with a stroke counter (base 3) by two expert evaluators (ICC = 0.97). The SL was calculated as SL = v•SF -1 (Craig & Pendergast, 1979) .
4
Swim Velocity Measurement Protocol
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A mechanical system to measure swimming velocity (Swim speedo-meter, Swimsportec, Hildesheim, Germany) was placed on a starting block in head-wall of the swimming pool. A cord was attached from the speedo-meter to the swimmer's hip. The signal was acquired at a frequency of 50Hz and transmitted by a 12-bit acquisition card (USB-6008, National Instruments, Austin, Texas, USA) to a software interface in LabView (v.2010. National Instruments, Austin, USA), which displays the speed-time data in real time. Data were then exported to a signal-processor software (AcqKnowledge v.3.5, Biopac Systems, Santa Barbara, USA and filtered with a 5Hz cut-off low-pass 4th order Butterworth filter upon residual analysis. The push-off from the wall (first five meters) and the finish (last meter) were discarded from the analysis to avoid the high variance in the motor behavior that are not caused by the swim stroke.
5
Measuring Arterial Pressure in Conscious Rats
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On the day of the experiment, an arterial catheter was connected to a pressure transducer (TSD104A) and a data acquisition unit (MP100 System; BIOPAC Systems Inc, Santa Barbara, CA, USA) to record the MABP and HR of conscious and freely moving rats. The data were converted and analyzed using AcqKnowledge v.3.9.0 software (BIOPAC Systems Inc, Santa Barbara, CA, USA). A quiet environment was maintained to avoid stress, and the rats had pulsatile arterial pressure recorded at baseline conditions for 30 min. After LPS or saline injection, MABP and HR were then measured as a single time point at 360 min. The results were expressed as the difference from baseline.
6
Speedo-meter for Swimming Biomechanics
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A speedo-meter cord (Swim speedo-meter, Swimsportec, Hildesheim, Germany) was attached to the swimmer's hip (Barbosa et al., 2013 (link)). The speedo-meter was placed on the forehead-wall of the swimming pool. A software interface in LabVIEW® (v. 2009) was used to acquire (f = 50 Hz), display, and process speed-time data for each trial. Data were transferred from the speedo-meter to the software by a 12-bit acquisition card (USB-6008, National Instruments, Austin, Texas, USA). Thereafter, data were exported to signal processing software (AcqKnowledge v. 3.9.0, Biopac Systems, Santa Barbara, USA) and filtered with a 5 Hz cut-off low-pass 4th order Butterworth filter, according to the analysis of the residual error vs. cut-off frequency output. Push-off start, dolphin kicks and the finish were discarded in the follow-up analysis. To run some of the non-linear parameters, at least 500 speed-time pairs are recommended to be collected (Yentes et al., 2013 (link)). As far as we understand, the speedo-meter is the most convenient device to do so, when benchmarked with other equipment available (e.g., motion-capture systems or inertia measurement units).
7
Measuring Swimmers' Propulsion Dynamics
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Propulsion data were acquired simultaneously with kinematic data. Pressure sensors (Swimming Technology Research, Richmond, VA, USA) were used to measure propulsion at a sampling rate of 100 Hz [22 ]. On each hand, sensors were placed between the third and fourth metacarpals to measure the pressure differential between the palmar and dorsal surfaces. This location is considered a good proxy for the point of application of the propulsion vector for the hand [23 (link)]. Swimmers were asked to keep their hands immersed at a depth of 0.50 m for 10 s to calibrate the system. This procedure was performed at the beginning of each trial. The pressure sensor data were transferred to the Aquanex software (Aquanex v. 4.2 C1211, Richmond, VA, USA) by an A/D converter [24 (link)]. Time-propulsion series were imported into signal-processing software (AcqKnowledge v. 3.9.0, Biopac Systems, Santa Barbara, CA, USA) for signal handling using a Butterworth 4th-order low-pass filter (cut-off: 5 Hz). For the dominant (Fmean_dominant, in N) and non-dominant (Fmean_non-dominant, in N) arm pull, the mean propulsion was measured. The intra-cyclic variation in propulsion of each upper limb (dFdominant and dFnon-dominant, in %) was calculated as being the CV, as previously mentioned. Identification of the swimmers’ hand dominance was made by self-report as reported by others [24 (link)].
8
Monitoring Hemodynamic Responses in Rats
9
Measuring Swimming Velocity and Variability
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A video camera (Sony FDR-X3000, Japan) synchronised with the mechanical apparatus recorded the swimmers in the sagittal plane to identify the entry and exit of the hand in the water. The string of a mechanical speedometer (SpeedRT, ApLab, Rome, Italy) was attached to the swimmers' waist (Dadashi et al., 2012) (link). The speedometer calculated the swimmer's displacement and velocity at a rate of 100 Hz and transferred data to a PC. Subsequently, the velocity-time series were imported into a signal processing software (AcqKnowledge v. 3.9.0, Biopac Systems, Santa Barbara, USA). Signal was handled with a Butterworth 4 th order low-pass filter (cut-off: 5 Hz) upon residual analysis. Swimming velocity (in m•s -1 ) was obtained from the software during three consecutive stroke cycles. Afterwards, the dv (in %) of each stroke cycle was computed as the CV, as previously mentioned (Barbosa et al., 2005) (link). Figure 1 presents the filtered swimming velocity for the three groups. Specifically, for each group, it shows the average of the 10 swimmers, being each of them represented by the mean of three consecutive stroke cycles. The beginning and end of each stroke cycle was considered the consecutive entry of the left hand into the water.
10
Quantifying Swimmer Thrust Dynamics
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Thrust was collected by an in-house customised system composed of differential pressure sensors and underwater camera. Sensors were placed between 3rd and 4th proximal phalanges of each hand (f = 50 Hz). The underwater camera was set on the headwall of the pool streaming images of the swimmer in the transverse plane (f = 50 Hz).
Swimmers were required to perform a 15-min general warm-up that included dynamic stretches, typical race-day routine, followed-up by familiarisation with the equipment. Then, data were collected at three different speeds (400 m race pace, 200 m race pace, all-out) with a 5-min rest interval between each bout (ICC = 0.90 ± 0.06) (Johnson, 1993) . Data were exported to a signal processing software (AcqKnowledge v3.9.1, Biopac Systems, Santa Barbara, CA, USA), where it was smoothed using a lowpass filter at 6 Hz after residual analysis. The dependent variables were: (1) mean thrust;
(2) peak thrust; and (3) peak power. These data were expressed from the average values of five left and right strokes each, at slow, moderate and all-out pace.
Swimmers were required to perform a 15-min general warm-up that included dynamic stretches, typical race-day routine, followed-up by familiarisation with the equipment. Then, data were collected at three different speeds (400 m race pace, 200 m race pace, all-out) with a 5-min rest interval between each bout (ICC = 0.90 ± 0.06) (Johnson, 1993) . Data were exported to a signal processing software (AcqKnowledge v3.9.1, Biopac Systems, Santa Barbara, CA, USA), where it was smoothed using a lowpass filter at 6 Hz after residual analysis. The dependent variables were: (1) mean thrust;
(2) peak thrust; and (3) peak power. These data were expressed from the average values of five left and right strokes each, at slow, moderate and all-out pace.
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