Labview programming software

Manufactured by National Instruments

LabVIEW is a graphical programming software developed by National Instruments. It is used for data acquisition, instrument control, and industrial automation. LabVIEW provides a visual programming environment that allows users to create and execute programs by manipulating block diagrams and front panels.

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Lab products found in correlation

Model mp150, by Biopac (2 mentions) Emg 100, by Biopac (1 mentions) Running wheel, by Lafayette Instrument (1 mentions)

6 protocols using labview programming software

Surface EMG for Limb Muscle Co-contraction

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Two separate bipolar surface EMG electrode (circular 20 mm diameter, silver/silver chloride, Biopac Systems, Inc., Santa Barbara, CA) orientations (20 mm interelectrode distances) were placed over the flexor carpi ulnaris and extensor digitorum muscles of the affected and non-affected forearms. The parallel electrode orientation was placed in accordance with the recommendations from the SENIAM Project [25 ]. The skin at each electrode site was carefully abraded and cleaned with alcohol, and the impedance was less than 2000 Ω. The EMG signal from each electrode arrangement was amplified (gain: × 1000) using differential amplifiers (EMG 100, Biopac Systems, Inc., Santa Barbara, CA, bandwidth 1.0–500 Hz).
The raw EMG signals were digitized at 1000 Hz and stored for subsequent analyses. All signal processing was performed using custom programs written with LabVIEW programming software (Student Version 8.5.1, National Instruments, Austin TX). The EMG signals were bandpass filtered (fourth-order Butterworth) at 10–500 Hz, and the amplitude (microvolts root-mean-square, μVrms) was calculated for a 2.0 s time period corresponding to the middle 33% of the 6-s isometric muscle action.
Co-contraction was calculated by the coactivation index (CI) and was expressed as percent activation of antagonist over agonist [1 (link)].

CI = \frac{EMG Antagonist}{EMG Agonist} * 100

Zuniga J.M., Dimitrios K., Peck J.L., Srivastava R., Pierce J.E., Dudley D.R., Salazar D.A., Young K.J, & Knarr B.A. (2018). Coactivation index of children with congenital upper limb reduction deficiencies before and after using a wrist-driven 3D printed partial hand prosthesis. Journal of NeuroEngineering and Rehabilitation, 15, 48.

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Voluntary Maternal Exercise During Pregnancy

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Exercised dams were housed individually in cages (35.3 × 23.5 × 20 cm) equipped with running wheels (12.7 cm in diameter; Lafayette Instrument, Lafayette, IN), and allowed access to the wheel during the 30 days prior to breeding. This approach allowed us to identify female mice who did not engage the running wheel activity on a daily basis. In our sample, all female mice maintained at least 1 h of exercise for every 24 h period. Once confirmation of pregnancy was established (see above), females in the exercise group were given access to the running wheel throughout the pregnancy. Once the dams delivered their pups, the wheel was removed from the cage. The wheel was electronically connected to a computer (Dell Inspiron E1705, Dell Inc., Round Rock, TX) and running activity was monitored hourly with custom LabVIEW programming software (version 2014, National Instruments, Austin, TX). This software records the exercise distance and duration of each dam for each hour during a 24-h period.

Liu J., Lee I., Feng H.Z., Galen S.S., Hüttemann P.P., Perkins G.A., Jin J.P., Hüttemann M, & Malek M.H. (2018). AEROBIC EXERCISE PRECONCEPTION AND DURING PREGNANCY ENHANCES OXIDATIVE CAPACITY IN THE HINDLIMB MUSCLES OF MICE OFFSPRING. Journal of strength and conditioning research, 32(5), 1391-1403.

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EMG Signal Processing and Analysis

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The raw EMG signals were digitized at 1,000 Hz, stored in a personal computer (MacBook Pro OSX, version 10.6.8, Apple Inc., Cuperino, CA) for subsequent analysis and processed with custom program, written with LabVIEW programming software (version 7.1, National Instruments, Austin, TX). The EMG signals were bandpass-filtered (zero phase shift, fourth-order Butterworth) at 10-500 Hz. Continuous 10 s epochs for the EMG AMP (microvolts root mean square, µVrms) and EMG MPF (MPF in Hz) were calculated. For the MPF analyses, each data segment was processed with a Hamming window and a discrete Fourier transform (DFT) algorithm in accordance with the recommendations of Hermens et al.[31 ]. The MPF was selected to represent the power spectrum on the basis of the recommendations of Hermens et al.[31 ] and was calculated as described by Kwatny et al.[34 (link)].

Bergstrom H.C., Housh T.J., Dinyer T.K., Byrd M.T., Jenkins N.D., Cochrane-Snyman K.C., Succi P.J., Schmidt R.J., Johnson G.O, & Zuniga J.M. (2020). Neuromuscular responses of the superficial quadriceps femoris muscles: muscle specific fatigue and inter-individual variability during severe intensity treadmill running. Journal of Musculoskeletal & Neuronal Interactions, 20(1), 77-87.

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Neuromuscular Biomarkers during Cycling Performance

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The raw EMG and MMG signals were sampled at 1 kHz with a 16-bit analog-to-digital converter (Model MP150, BIOPAC Systems, Inc., Santa Barbara, CA, USA). The signals were recorded and stored in a personal computer and the amplitude (microvolts root mean square, µVrms) values were calculated off-line using a custom program written with LabVIEW programming software (version 8.5, National Instruments, Austin, TX). The EMG and MMG signals were zero-meaned and bandpass filtered (fourth-order Butterworth) at 10-500 Hz and 5-100 Hz, respectively. The EMG and MMG amplitude (root mean square; AMP) and frequency (mean power frequency; MPF) values were calculated for 10 second epochs throughout the GXT as well as the CP_-10% and CP_+10% rides. The EMG AMP, EMG MPF, MMG AMP, and MMG MPF were recorded during the CP_-10% and CP_+10% rides and were normalized as a percent change from the 5% timepoint of each respective ride.

Dinyer T.K., Byrd M.T., Cochrane-Snyman K.C., Jenkins N.D., Housh T.J., Schmidt R.J., Johnson G.O, & Bergstrom H.C. (2019). Time course of changes in neuromuscular responses during rides to exhaustion above and below critical power. Journal of Musculoskeletal & Neuronal Interactions, 19(3), 266-275.

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Electro- and Mechanomyographic Signal Analysis

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The raw electromyographic and mechanomyographic signals were digitized at 2000 Hz with a 16-bit analog-to-digital converter (Model MP150, BIOPAC Systems, Inc., Santa Barbra, CA) and stored in a personal computer for subsequent analysis. All signal processing was performed using custom programs written with LabVIEW programming software (Version 13.0, National Instruments, Austin TX). The electromyographic and mechanomyographic signals were zero-meaned and bandpass filtered (fourth-order Butterworth) at 10-500 Hz and 5-100 Hz, respectively. The electromyographic and mechanomyographic amplitude and frequency values were calculated from 2-s epochs corresponding to the middle 33% of each MVIC and normalized to the initial MVIC values.

Smith C.M., Housh T.J., Hill E.C., Cochrane K.C., Jenkins N.D., Schmidt R.J, & Johnson G.O. (2016). Effects of fatiguing constant versus alternating intensity intermittent isometric muscle actions on maximal torque and neuromuscular responses. Journal of Musculoskeletal & Neuronal Interactions, 16(4), 318-326.

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EMG Fatigue Threshold Protocol

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The EMG FT was determined for each subject at each visit. Briefly, the absolute EMG amplitude (microvolts root mean square, µVrms) was calculated from 6 data points for each 1-minute stage and then plotted vs. time (Figure 1) ( 5,10,14,22 ). Specifically, each data point represents a 10-second epoch in which all completed bursts within that window were selected for signal processing. The raw EMG signals were digitized at 1,000 Hz and stored in a personal computer (Dell Inspiron E1705; Dell Inc., Round Rock, TX) for subsequent analysis ( 10,13,26,27 ). All signal processing was performed using custom programs written with LabVIEW programming software (version 2014; National Instruments, Austin, TX). The EMG signals were bandpass-filtered (fourth-order Butterworth) at 10-500 Hz. Thus, for each power output, the EMG amplitude vs. time relationship was analyzed using linear regression (Figure 1). The EMG FT is a derived value from the average of the highest power output with a nonsignificant slope coefficient and the lowest power output with a significant positive slope coefficient ( 5,6,10,12-14,22 ). F1 Figure 1.: Graphical representation of the EMG FT for a single subject's responses to the no music and music conditions.

Centala J., Pogorel C., Pummill S.W, & Malek M.H. (2020). Listening to Fast-Tempo Music Delays the Onset of Neuromuscular Fatigue. Journal of strength and conditioning research, 34(3).

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