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«A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural Mechanical College in partial fulfillment of the ...»

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electrophysiological signals. Its applications include, but are not limited to: biomechanics, work physiology, and kinesiology. EMG calculations evaluate muscle activity, fatigue, and stress during the entire duration of the exercise. Delsys Inc. (2009) explains the functioning process and equipment components.

Software CD: once installed, it allows the computer to interpret data received

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computer for real-time display and storage. The touch screen panel allows the user to control and maneuver the experiment, along with On/Off and Reset buttons and LCD Backlight. An amber LED helps obtain feedback regarding battery recharging. The top of the Main Unit has connectors for the Docking Module and the Input Module Cables, but both connections may not be established simultaneously due to safety reasons.

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DE-2.3 EMG Sensor: housed in polycarbonate, it subtracts EMG potentials detected at two distinct locations on the surface of the skin directly above an active muscle. EMG potentials from the electrode reflect the electric potential of a neutral site located away from the EMG muscle source, with a 20-450 Hz

bandwidth:

Figure 5.4: DE-2.

3 Single Differential Surface EMG Sensor Source: Delsys, Inc. (2009)

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Delsys Inc. (2009) further explains: “The surface EMG signal is the result of the potential difference between V1 and V2 on the skin surface […] The curved enclosure geometry is designed to maximize skin contact and adhesion while minimizing the negative effects of sweat during vigorous activities,” thereby ensuring accuracy in data collection during exercise.

Docking Module 2 Input Module (1/2) 6 1GB SD Memory Card: connects with up to 8 sensors and with the electrodes‟ cables. The memory card stores the EMG data for future use and reference. Users may clip it on a belt or lumbar pack.

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D-Link WUA-1340 Wireless G USB Adapter: connects the EMG apparatus with the computer without the need of a cable, providing the participant with freedom

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EMG pads (electrodes) are placed on the participant‟s left tibialis anterior and the left medial gastrocnemius, assuming walking is a relatively symmetric task. The input module is

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Figure 5.9 shows that the electrodes must be parallel to the muscle fibers for accurate reception of the signals from muscle activity during exercise (Figure 5.

8). The next diagram

depicts the alignment with the muscle under the skin:

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A key defining characteristic of the digital signal is its sampling rate, or sample rate. This refers to the frequency of signal measurement during the experiment. The more frequently the signal is sampled, the more accurate is its interpretation. However, the higher the sampling rate, the more storage memory is needed. EMG results are displayed in the form of an electromyogram, a graph that enables the user to interpret data from the electric signals. For this thesis, a sampling rate of 500 Hz was used, for a recording duration of one minute for each walking drill.

The next step to interpreting EMG data is to normalize it by demeaning the EMG signal, rectifying the full-wave, normalizing according the highest reading, and averaging it to the mean average value (MAV). The MAV therefore constitutes the type of data outcome. EMG data can be particularly helpful when translating the electromyogram into an Excel graph.

5.3.3. Body Maps In addition, the discomfort the subjects experience is specified by using body map parts (see Figure 5.10). After each test, each participant was asked to verbally number the level of pain they felt on areas 20 to 27 (knees, calves, ankles, and feet), which correspond to the extrinsic foot muscles tested and to areas where athletes commonly complain of pain or discomfort.

Discomfort is described as the exertion level on Borg‟s ergonomic scale, that is, a scale from 0 to 10, 0 being no discomfort at all, at 10 being almost absolutely unbearable. Figure 5.11 provides a legend for the quantification of the discomfort felt.

The Borg CR-10 scale, named after the tool developed by Gunnar Borg (1998) to measure intensity of experience in the field of perceived exertion, measures the intensity of the sensation perceived. It is category-ratio scaling that combines Steven‟s ratio scaling,that is, the “interval scale in which distances are stated with respect to a rational zero rather than with respect to, for example, the mean" (Nunnally, 1967), and psychophysical category scaling (Galanter and Jacobs, 1972).

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5.4. Nature of the Data The data obtained from the EMG experiment shows the difference in voltage resulting from muscle activity. The data obtained from the body maps, though subjective, is an assessment of the comfort the removable heel offers the athlete. The comfort rates disclosed by the subjects are as essential as the more technology-based EMG, in the sense that by assessing pain and discomfort, they provide a sense of the athlete‟s ability to perform at maximum capacity, once freed from the discomfort hinderance.





5.5. Hypothesis: Statement and Parameters 5.5.1. Hypothesis Testing

Experimental design:

 Dependent variables: normalized EMG data readings for gastrocnemius and tibialis

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 Independent variables: presence or absence of heel and 2 levels of walking speed, that is, either walking at 2 mph or 3 mph for each of the muscles tested.

For each case, the null statistical hypothesis, denoted, refers to the absence of significant change. The tested alternative hypothesis, denoted, is tested as the significant change desired to be demonstrated.

Hypothesis 1:

: There is no significant change in EMG reading data from walking without heels to walking with heels.

: There is a significant muscle activity change as revealed by the EMG reading, from walking without heels to walking with heels.

Hypothesis 2:

: There is no significant change in EMG activity when speed changes from 2mph to 3mph.

: There is significant change in EMG activity when speed changes from 2mph to 3mph.

Hypothesis 3:

: The mean average of discomfort is the same for all participants before and after adding the heel.

: The mean average of discomfort after adding the heel is not equal to the mean average of discomfort before adding the heel.

5.5.2. Statistical Analysis Regarding the first and second hypotheses, ANOVA calculations reveal the variance – the difference in muscle activity as shown by the EMG when the subject uses the heel vs. when the subject does not – and thus statistically analyzes the effect of walking speed and presence or absence of heel on the dependent variables. The p value is calculated statistically, using ANOVA with the EMG data collected. Based on the p value obtained from the EMG data collected regarding the activity of each subject‟s gastrocnemius and tibialis anterior muscles, the null hypothesis is rejected. The rejection region is the range of p values greater than 0.05.

Subsequently, a significant change is considered to correspond to a p value lesser than or equal to 0.05, and the alternative hypothesis will be correct, if the p value is lesser than or equal to 0.05.

The third hypothesis was tested through SAS 9.1 English version. A one-way ANOVA test was used to identify any significant difference in the level of discomfort before and after adding the heel during the experiment, with a p value establishing the rejection criteria at 0.05.

The following equation determined the test results:

For which

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j=1,2,3,4 represents the different body areas k=1,2 where 1=before adding the heel and 2=after adding the heel 5.5.3. Steps for Data Processing

1. EMG data was normalized, that is, made to fit into a bell curve, through the following steps:

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2. Proceeding to full-wave rectification

3. Normalizing with respect to the maximum (highest EMG reading)

4. Averaging to determine the mean absolute value (MAV)

2. The root mean square was calculated in an Excel spreadsheet format

3. The root mean square for each subject and each activity was synthesized and analyzed.

4. Statistical analysis: Statistix 9.0 and SAS 9.1 softwares were used to analyze the variance of muscle activity in the gastrocnemius and tibialis anterior with heels and without heels, as well as the discomfort difference in all areas of the body map before and after installation of

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Differences in EMG values from values reveal a noticeable change in muscle activity

from walking without the removable heel to walking with it, as shown in the following table:

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Figure 6.1 shows that when walking with heels at 2 mph, the EMG reveals that the participants‟ level of activity in the tibialis and subsequent muscle fatigue decreases 21.

5% from walking at 2 mph without heels on average. Fatigue in the gastrocnemius decreases 23.5% when the heel is installed while walking at 2 mph (see Figure 6.4).

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Figure 6.2 shows that during the 3 mph experiment, adding the heel generated a muscle activity decrease of 22.

5% on average in the tibialis and 25% in the gastrocnemius on average.

Thus, all participants experienced an activity decrease in the tibialis during both walking experiments when the heel is added (see Figure 6.4).

Figures 6.3 and 6.

4 are comparative graphs of the difference of results according to speed. They indicate greater difference in muscle activity with use of the heel as the speed increases. In all categories, the tibialis muscle seems to be used to a greater extent than the gastrocnemius is in terms of volts. The tibialis muscle is also the muscle that manifests a greater difference in intensity of use, according to the voltage, when the removable heel is installed to the bottom of the spike shoes.

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6.1.1. Muscles Evaluated The EMG graphs indicate greater muscle activity in the tibialis anterior than in the gastrocnemius, but there seems to be no proportionality in speed or presence or absence of heel.

6.1.2. Impact of Speed on Muscle Activity EMG indicates a greater muscle activity in the tibialis anterior and the gastrocnemius, as the participants walk faster. Additionally, Figures 6.3 and 6.4 show that the removable heel provides more fatigue relief as the participants walk faster. This is due to the fact that the shock absorption at the heel increases with speed. Additional research could determine the proportionality equation to identify the effect of speed on muscle activity difference according to the presence or absence of heel.

6.1.3. Impact of the Participant‟s Running Background In a general manner, the EMG calculations reveal that not wearing the heel brings about significantly greater muscle fatigue for both muscles. One must note, however, that trained sprinters are used to practicing in spikes without heels. On the other hand, practicing on the toes is harder for distance runners, since during regular practice, they need to use their heels more than sprinters. As the difference between activity with and without heel is averaged for all participants, and then categorized in accordance with their athletic backgrounds, the following

averaged results are obtained:

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Among athletes, the heel benefits distance runners the most. Such an observation is not surprising. Effectively, tibialis and gastrocnemius fatigue is a problem occurring mostly among this group of athletes, based on the techniques with which they run and the duration of their performances. This group of runners performs in dorsiflexion and therefore needs extra support in their heels. The Nike spikes, used alone, cannot provide such support without the removable heel. Brukner et al., in providing definitions for different running gaits, present an insight into understanding the discrepancy of result values between participants of different running backgrounds. Long and mid-distance runners tend to have both heels and forefeet striking the ground together at the beginning of the contact stage of stance. On the other hand, sprint runners‟ forefeet, most of the time, present the only contact with the ground throughout the stance phase (thus the curved shape of the spike shoe) (Brukner et al.).

The following diagrams illustrate this fact more remarkably in that they emphasize the contrast between each category of participants. Figures 6.5 through 6.8 display the averaged percent decrease in each muscle and each walking speed – 2 mph and 3 mph.

Nevertheless, the experiment is conducted for only one minute. In the future, one should consider making additional, longer experiments, in order to ensure that the results are not only consistent, but also consistently conclusive.

30% 25%

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Figure 6.7: Percent decrease in gastrocnemius activity for each participant group 40% 35% 30%

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6.1.4. Statistical Results Table 6.3 contains the results of ANOVA of the electromyographic data obtained from statistical calculations. It displays change across both speeds (2 and 3 mph). The term “locomotion” refers to the speeds at which the participants walk (2 or 3 mph). “Subject” refers to the participants. The p value that determines whether the hypothesis is true or not is noted under the last column, that is, the column labeled “P.” Table 6.3: Analysis of variance for gastrocnemius and tibialis anterior activity Analysis of Variance Table for Gastrocnemius

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