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2007, Piran, Slovenia

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Environmental Ergonomics XII<br />

Igor B. Mekjavic, Stelios N. Kounalakis & Nigel A.S. Taylor (Eds.), © BIOMED, Ljubljana <strong>2007</strong><br />

the middle point of the muscle belly, and fixed with adhesive tape (3M, USA). A<br />

reference electrode was placed on the lliac crest.<br />

EMG signals were recorded with a multitelemetry system (SYNA ACT MT11, NEC,<br />

Japan) and were digitised (analogue/digital converter, PowerLab/16c, ADI<br />

Instruments, USA) prior to storing the information on a personal computer (Vaio<br />

Z1/T, SONY, Japan). EMG signals were recorded at a sampling rate of 2000 Hz.<br />

Analysis of the EMG signals was conducted using Chart software (ADI Instruments).<br />

Raw data were digitally filtered using a band-pass filter (10-500 Hz), and numerically<br />

rectified. The data was subsequently low-pass filtered with a second-order<br />

Butterworth filter with a cut-off frequency at 10 Hz. The signals were then timeinterpolated<br />

over a time base with 100 points for individual walking cycles. In each<br />

trial for a given walking speed, the EMG of 15 walking cycles were averaged, and<br />

integrated EMG (iEMG). The value of iEMG was normalized for each muscle’s<br />

iEMG observed during the 2 km/h walk in the normoxic environment.<br />

Foot switch<br />

Ground contact information was recorded with a specially designed and the<br />

information stored together with the EMG information on a personal computer. The<br />

sensor comprised a tape switch (LA-150, Tokyo Sensor Co, Japan), which responded<br />

immediately to heel contact once the force exceeded 15N. This information<br />

determined the onset of the walking cycle, and was used to time-interpolate the EMG<br />

as described above.<br />

Statistical analysis<br />

Values are expressed as means (SD). A one way repeated measures analysis of<br />

variance (ANOVA) was used to compare the observed iEMG during walking at the<br />

different speeds in the normoxic and hypoxic environments Statistical significance<br />

was accepted at P < 0.05 (SPSS 11.0j for windows, SPSS Japan Inc, Japan).<br />

RESULTS<br />

Figure 1 represents typical EMG data recorded from the muscles while walking at<br />

each designated speed. EMG activity in both conditions increased with increasing<br />

walking speed. In normoxia, iEMG of SOL increased 1.46 (0.25) times, MG by 1.75<br />

(0.25) times, TA by 2.09 (0.59) times, BF by 1.94 (0.31) times, and VM by 2.28<br />

(0.73) times. In hypoxia, iEMG of SOL increased by 2.37 (1.17) times, MG by 1.80<br />

(0.58) times, TA by 2.56 (1.19) times, BF by 2.33 (0.90) times, and VM by 3.76<br />

(1.03) times (see Figure 2).<br />

Comparison of the EMG data for the 2 km/h walking speeds in normoxia and<br />

hypoxia, revealed that only the activity in MG was significantly higher (p < 0.05) in<br />

hypoxia than normoxia (p < 0.05). There were no significant difference in the other<br />

muscles at this speed. Similarly, for the 4 km/h walking speed, only the activity in BF<br />

was significantly higher in hypoxia (p < 0.05) . At 6km/h there were no significant<br />

differences in EMG between the normoxic and hypoxic conditions Figure 2).<br />

94

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