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Muscles play a vital role in the movement of the body. Muscle tissue can be differentiated into three different types: skeletal muscle, cardiac muscle and smooth muscle. Skeletal muscles are present in the muscles attached to the bones. The spinal cord or the brain stimulates the motor units, thus signaling the skeletal muscles to contact. The contraction of the skeletal muscles helps to perform mechanical work. The number of motor neuron activated by the brain is directly proportional to the amount of work to be done.
When the skeletal muscle performs repetitive activities, it will lead to fatigue. Fatigue is a condition in which the skeletal muscles ability to generate force is decreased. Fatigue occurs when the energy uptake rate by the muscles is faster than the energy generation rate. During the process of contraction, the skeletal muscles converts the chemical energy into mechanical and thermal energy.
Electromyography is the process by which the electrical activity of the skeletal muscles is recorded.
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The skin voltage produced by the skeletal muscles is detected, amplified and recorded i this process. The final graph obtained after the recording is known as the electromyogram.
The two principle bioelectric activities involved in recording EMG signal are:
Hypothesis:
H0: The time to fatigue is independent of dominant and the non-dominant arms.
H1: The fatigue time period varies depending on whether the arm is dominant or non-dominant.
HA: The force at peak is same for both the dominant and the non-dominant arm.
HB: The force at peak is greater for the dominant arm and less for the non-dominant arm.
Materials required:
The Computer setup was done first, and the cables were plugged as per the instruction. The subject was made ready for the EMG data recording by cleaning the oily skin. Three electrodes were attached to each forearm. The Electrode lead set was clipped to dominant’s forearm according to the color code. Then the subject is in seated position and holds the dynamometer with the dominant hand. For the calibration, the subject clenched the hand dynamometer with the dominant hand as hard as possible for 2 sec and after that clenching was stopped. The data was recorded, and further steps were continued.
For the data recording process, the dominant arm was used first. A series of clench-release-wait cycles were performed. The clench was held for 2 seconds. The force was increased in each clench cycle. The data obtained as a graph was then recorded. Then again, data for fatigue was recorded. For this process, the subject clenched the dynamometer with possible maximum force. The clenching was continued until the force had decreased by 50%. The same steps were done for the non-dominant arm. Data was recorded for 2 subjects. The obtained data was then analyzed and put into excel file.
The average time of fatigue for the dominant arm was found to be 28.64 sec. The median was found to be 18.157. And, the standard deviation was found to be 23.56. For the non-dominant hand, the average time of fatigue, median and standard deviation were found to be 23.61, 14.69 and 19.97 respectively.
For the first set of hypotheses, using t-test, the p-value was found to be 0.0978.
The average force for the dominant hand was found to be 4333.96. The median was 3689.3t and the standard deviation was found to be 2618.93. For the non-dominant hand, the mean force, median and standard deviation were found to be 3756.54, 3440.85 and 2369.76 respectively.
For the second set of hypotheses, using t-test, the p-value was found to be 1.79*10-7.
For our first set of hypotheses, the p-value was found to be 0.0978. This value suggests that we failed to reject our null hypothesis. Thus, we can-not conclude that the fatigue time period depends on the dominant or non-dominant hand. However, research and studies suggest that the fatigue time period is somehow dependent on the arm that we use. We prefer to use the dominant hand more than the non-dominant hand. Thus, this use of muscles causes more fatigue resistance in structures of the neuromuscular system. Also, some research suggest that it is difficult to engage the muscle fibers of non-dominant arm into carrying muscular activity, hence they get fatigued.
For our second set of hypotheses, the p-value was found to be 1.79*10-7. Thus, we can reject our null hypothesis and conclude that force at peak is grater for the dominant arm than the non-dominant arm. Many other lab activities conducted in the past also shows that the force at peak is grater for the dominant arm than the non-dominant arm. One article suggests that, the dominant hand is 10% stronger than the non-dominant hand. This is one of the reasons why the force at peak is higher for the dominant arm as compared to the non-dominant arm. Some research however suggests that there is no significant difference in the strength of the dominant and the non-dominant arm.
There might have been various errors while performing our experiments. One error may be while we connect the electrodes in our body. If these are not connected properly, there are high chances of getting error in the data. Data analysis is also an important part of the experiment. The data should be analyzed properly to prevent the errors in the experiment.
Muscle Fatigue and Force Disparity Between Dominant and Non-Dominant Arms: An EMG Study. (2024, Feb 22). Retrieved from https://studymoose.com/document/muscle-fatigue-and-force-disparity-between-dominant-and-non-dominant-arms-an-emg-study
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