Accuracy of neuro-fuzzy logic and regression calculations in determining maximal lactate steady-state power output from incremental tests in humans

2002 ◽  
Vol 88 (3) ◽  
pp. 264-274 ◽  
Author(s):  
Gerhard Smekal ◽  
Arno Scharl ◽  
Serge von Duvillard ◽  
Rochus Pokan ◽  
Arnold Baca ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Power Output ◽  
State Power ◽  
Maximal Lactate Steady State ◽  
Neuro Fuzzy

FAILURE OF NEURO-FUZZY LOGIC AND REGRESSION CALCULATIONS TO DETERMINE MAXIMAL LACTATE STEADY STATE- LACTATE CONCENTRATION FROM INCREMENTAL TESTS

2003 ◽  
Vol 35 (Supplement 1) ◽  
pp. S224
Author(s):  
G Smekal ◽  
P Pazdera ◽  
A Baca ◽  
S P. von Duvillard ◽  
R Pokan ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Lactate Concentration ◽  
Maximal Lactate Steady State ◽  
Neuro Fuzzy

Exercise slightly above the maximal lactate steady state reduces self-efficacy and increases negative affect

2019 ◽  
Author(s):  
James Graeme Wrightson ◽  
Louis Passfield
Keyword(s):  
Steady State ◽  
Negative Affect ◽  
Power Output ◽  
Self Efficacy ◽  
Repeated Measures ◽  
Physiological State ◽  
Constant Load ◽  
Peak Power Output ◽  
Time To Exhaustion ◽  
Maximal Lactate Steady State

Objectives: To examine the effect of exercise at and slightly above the maximal lactate steady state (MLSS) on self-efficacy, affect and effort, and their associations with exercise tolerance.Design: Counterbalanced, repeated measures designMethod: Participants performed two 30‐minute constant‐load cycling exercise at a power output equal to that at MLSS and 10 W above MLSS, immediately followed by a time‐to‐exhaustion test at 80% of their peak power output. Self-efficacy, affect and effort were measured before and after 30 minutes of cycling at and above MLSS.Results: Negative affect and effort higher, and self-efficacy and time to exhaustion were reduced, following cycling at MLSS + 10 W compared to cycling at the MLSS. Following exercise at the MLSS self-efficacy, affect and effort were all associated with subsequent time-to exhaustion. However, following exercise at MLSS + 10 W, only affect was associated with time-to exhaustion. Conclusions: Self efficacy, affect and effort are profoundly affected by physiological state, highlighting the influence of somatic states on perceptions and emotions during exercise. The affective response to exercise appears to be associated with exercise tolerance, indicating that the emotional, as well as physiological, responses should be considered when prescribing exercise training.

Slight power output manipulations around the maximal lactate steady state have an impact on fatigue in females and males.

2021 ◽  
Author(s):  
Rafael de Almeida Azevedo ◽  
Jonas Forot ◽  
Danilo Iannetta ◽  
Martin J. MacInnis ◽  
Guillaume Y. Millet ◽  
...  
Keyword(s):  
Steady State ◽  
Power Output ◽  
Contractile Function ◽  
Femoral Nerve ◽  
Peak Torque ◽  
Knee Extensors ◽  
Time To Exhaustion ◽  
Maximal Lactate Steady State ◽  
Subsequent Performance ◽  
Single Twitch

Neuromuscular fatigue (NMF) and exercise performance are affected by exercise intensity and sex differences. However, whether slight changes in power output (PO) below and above the maximal lactate steady-state (MLSS) impact NMF and subsequent performance (time to exhaustion, TTE) is unknown. Purpose: This study compared NMF and TTE in females and males in response to exercise performed at MLSS, 10 W below (MLSS-10) and above (MLSS+10). Methods: Twenty participants (9 females) performed three 30-min constant-PO exercise bouts followed (1 min delay) by a TTE at 80% of the peak-PO. NMF was characterized by isometric maximal voluntary contractions (IMVC) and femoral nerve electrical stimulation of knee extensors [e.g. peak torque of potentiated high-frequency (Db100) and single twitch (TwPt)] before and immediately after the constant-PO and TTE bouts. Results: IMVC declined less after MLSS-10 (-18±10%) compared to MLSS (-26±14%) and MLSS+10 (-31±11%) (all p<0.05), and the Db100 decline was greater after MLSS+10 (-24±14%) compared to the other intensities (MLSS-10: -15±9%; MLSS: -18±11%) (all p<0.05). Females showed smaller reductions in IMVC and TwPt compared to males after constant-PO bouts (all p<0.05), this difference being not dependant on intensity. TTE was negatively impacted by increasing the PO in the constant-PO (p<0.001), with no differences in end-exercise NMF (p>0.05). Conclusion: Slight changes in PO around MLSS elicited great changes in the reduction of maximal voluntary force and impairments in contractile function. Although NMF was lower in females compared to males, the changes in PO around the MLSS impacted both sexes similarly.

Comparison of Power Outputs During Time Trialing and Power Outputs Eliciting Metabolic Variables in Cycle Ergometry

2010 ◽  
Vol 20 (2) ◽  
pp. 115-121
Author(s):  
David Michael Morris ◽  
Rebecca Susan Shafer
Keyword(s):  
Steady State ◽  
Blood Lactate ◽  
Power Output ◽  
Lactate Threshold ◽  
Time Trial ◽  
Cycle Ergometry ◽  
Moderate Altitude ◽  
Maximal Lactate Steady State ◽  
Metabolic Variables ◽  
Power Outputs

The authors sought to compare power output at blood lactate threshold, maximal lactate steady state, and pH threshold with the average power output during a simulated 20-km time trial assessed during cycle ergometry. Participants (N = 13) were trained male and female cyclists and triathletes, all permanent residents at moderate altitude (1,525–2,225 m). Testing was performed at 1,525 or 1,860 m altitude. Power outputs were determined during a simulated 20-km time trial (PTT), at blood pH threshold (PpHT), at maximal lactate steady state (PMLSS), and at blood lactate threshold determined by 2 methods: the highest power output that did not result in consecutive and continued increases in blood lactate concentrations from exercising baseline (PLT) and the highest power output that did not result in consecutive and continued increases of ≥1 mmol/L in blood lactate concentrations from exercising baseline (PLT1). PLT, PLT1, and PMLSS were all significantly lower than PpHT (p < .05) and PTT (p < .05). No significant difference was observed between PpHT and PTT (p > .05). Significant correlations were observed between each of the metabolic variables, PLT, PLT1, PMLSS, and PpHT, compared with PTT (p < .05). The authors conclude that, of the 4 metabolic variables, only PpHT offered an accurate reflection of PTT.

scholarly journals Maximal Lactate Steady State Versus the 20-Minute Functional Threshold Power Test in Well-Trained Individuals: “Watts” the Big Deal?

2020 ◽  
Vol 15 (4) ◽  
pp. 541-547 ◽  
Author(s):  
Erin Calaine Inglis ◽  
Danilo Iannetta ◽  
Louis Passfield ◽  
Juan M. Murias
Keyword(s):  
Steady State ◽  
Field Test ◽  
Power Output ◽  
Constant Load ◽  
Test Series ◽  
Threshold Power ◽  
Maximal Lactate Steady State ◽  
Large Variability ◽  
Power Test ◽  
Induced Changes

Purpose: To (1) compare the power output (PO) for both the 20-minute functional threshold power (FTP20) field test and the calculated 95% (FTP95%) with PO at maximal lactate steady state (MLSS) and (2) evaluate the sensitivity of FTP95% and MLSS to training-induced changes. Methods: Eighteen participants (12 males: 37 [6] y and 6 females: 28 [6] y) performed a ramp-incremental cycling test to exhaustion, 2 to 3 constant-load MLSS trials, and an FTP20 test. A total of 10 participants returned to repeat the test series after 7 months of training. Results: The PO at FTP20 and FTP95% was greater than that at MLSS (P = .00), with the PO at MLSS representing 88.5% (4.8%) and 93.1% (5.1%) of FTP and FTP95%, respectively. MLSS was greater at POST compared with PRE training (12 [8] W) (P = .002). No increase was observed in mean PO at FTP20 and FTP95% (P = .75). Conclusions: The results indicate that the PO at FTP95% is different to MLSS, and that changes in the PO at MLSS after training were not reflected by FTP95%. Even when using an adjusted percentage (ie, 88% rather than 95% of FTP20), the large variability in the data is such that it would not be advisable to use this as a representation of MLSS.

NEURO-FUZZY MODELING TO CALCULATE MAXIMAL LACTATE STEADY STATE

1999 ◽  
Vol 31 (Supplement) ◽  
pp. S377
Author(s):  
G. Smekal ◽  
A. Scharl ◽  
R Pokan ◽  
E. Lane ◽  
R Baron ◽  
...  
Keyword(s):  
Steady State ◽  
Fuzzy Modeling ◽  
Maximal Lactate Steady State ◽  
Neuro Fuzzy

Are Peak Oxygen Uptake and Power Output at Maximal Lactate Steady State Obtained from a 3-Min All-Out Cycle Test?

2011 ◽  
Vol 32 (06) ◽  
pp. 433-437 ◽  
Author(s):  
B. Sperlich ◽  
M. Haegele ◽  
A. Thissen ◽  
J. Mester ◽  
H. -C. Holmberg
Keyword(s):  
Steady State ◽  
Oxygen Uptake ◽  
Power Output ◽  
Peak Oxygen Uptake ◽  
Maximal Lactate Steady State

Heart Rate-based Lactate Minimum Test in Running and Cycling

2021 ◽  
Author(s):  
Claudio Perret ◽  
Kathrin Hartmann
Keyword(s):  
Heart Rate ◽  
Steady State ◽  
Oxygen Uptake ◽  
Exercise Intensity ◽  
Exercise Test ◽  
Power Output ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Maximal Lactate Steady State ◽  
Lactate Minimum

AbstractThe heart rate-based lactate minimum test is a highly reproducible exercise test. However, the relation between lactate minimum determined by this test and maximal lactate steady state in running and cycling is still unclear. Twelve endurance-trained men performed this test in running and cycling. Exercise intensity at maximal lactate steady state was determined by performing several constant heart rate endurance tests for both exercise modes. Heart rate, power output, lactate concentration, oxygen uptake and rating of perceived exertion at lactate minimum, maximal lactate steady state and maximal performance were analysed. All parameters were significantly higher at maximal lactate steady state compared to lactate minimum for running and cycling. Significant correlations (p<0.05) between maximal lactate steady state and lactate minimum data were found. Peak heart rate and peak oxygen uptake were significantly higher for running versus cycling. Nevertheless, the exercise mode had no influence on relative (in percentage of maximal values) heart rate at lactate minimum (p=0.099) in contrast to relative power output (p=0.002). In conclusion, all measured parameters at lactate minimum were significantly lower but highly correlated with values at maximal lactate steady state in running and cycling, which allows to roughly estimate exercise intensity at maximal lactate steady state with one single exercise test.

Continuous Estimation of CO2 Production during Exercise

1997 ◽  
Vol 36 (04/05) ◽  
pp. 368-371
Author(s):  
R. Soma ◽  
Y. Yamamoto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Infusion Rate ◽  
Feedback System ◽  
Whole Body ◽  
Co2 Production ◽  
Cycle Exercise ◽  
Continuous Estimation ◽  
Breath Measurement ◽  
13Co2 Enrichment

Abstract.A new method was developed for continuous isotopic estimation of human whole body CO2 rate of appearance (Ra) during non-steady state exercise. The technique consisted of a breath-by-breath measurement of 13CO2 enrichment (E) and a real-time fuzzy logic feedback system which controlled NaH13CO3 infusion rate to achieve an isotopic steady state. Ra was estimated from the isotope infusion rate and body 13CO2 enrichment which was equal to E at the isotopic steady state. During a non-steady state incremental cycle exercise (5 w/min or 10 w/min), NaH13CO3 infusion rate was successfully increased by the action of feedback controller so as to keep E constant.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

Sign in / Sign up

Export Citation Format

Share Document