Normalising the electromyography (EMG) signal as a percentage of maximum voluntary contraction (%MVC) serves several important purposes:

1. **Comparison Across Individuals**: Normalising EMG data allows for comparison across different individuals. This is because muscle strength and EMG signal amplitude can vary greatly between individuals due to factors such as muscle size, subcutaneous fat, and electrode placement. By expressing EMG signals as a percentage of an individual's maximum voluntary contraction, these differences can be accounted for, allowing for more meaningful comparisons.

2. **Comparison Across Muscles**: Similarly, normalising EMG data allows for comparison across different muscles within the same individual. Different muscles can produce different amounts of electrical activity, so normalising allows for a fair comparison.

3. **Accounting for Fatigue**: Normalising EMG data can help account for the effects of fatigue. As a muscle fatigues, the amplitude of its EMG signal can decrease. By expressing EMG signals as a percentage of the MVC, the effects of fatigue can be accounted for.

4. **Standardisation**: Normalising EMG data provides a standardised measure of muscle activation, which can be useful in both research and clinical settings. For

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Normalising the electromyography (EMG) signal as a percentage of maximum voluntary contraction (%MVC) serves several important purposes:

1. **Comparison Across Individuals**: Normalising EMG data allows for comparison across different individuals. This is because muscle strength and EMG signal amplitude can vary greatly between individuals due to factors such as muscle size, subcutaneous fat, and electrode placement. By expressing EMG signals as a percentage of an individual's maximum voluntary contraction, these differences can be accounted for, allowing for more meaningful comparisons.

2. **Comparison Across Muscles**: Similarly, normalising EMG data allows for comparison across different muscles within the same individual. Different muscles can produce different amounts of electrical activity, so normalising allows for a fair comparison.

3. **Accounting for Fatigue**: Normalising EMG data can help account for the effects of fatigue. As a muscle fatigues, the amplitude of its EMG signal can decrease. By expressing EMG signals as a percentage of the MVC, the effects of fatigue can be accounted for.

4. **Standardisation**: Normalising EMG data provides a standardised measure of muscle activation, which can be useful in both research and clinical settings. For

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Normalising the electromyography (EMG) signal as a percentage of maximum voluntary contraction (%MVC) serves several important purposes:

1. **Comparison Across Individuals**: Normalising EMG data allows for comparison across different individuals. This is because muscle strength and EMG signal amplitude can vary greatly between individuals due to factors such as muscle size, subcutaneous fat, and electrode placement. By expressing EMG signals as a percentage of an individual's maximum voluntary contraction, these differences can be accounted for, allowing for more meaningful comparisons.

2. **Comparison Across Muscles**: Similarly, normalising EMG data allows for comparison across different muscles within the same individual. Different muscles can produce different amounts of electrical activity, so normalising allows for a fair comparison.

3. **Accounting for Fatigue**: Normalising EMG data can help account for the effects of fatigue. As a muscle fatigues, the amplitude of its EMG signal can decrease. By expressing EMG signals as a percentage of the MVC, the effects of fatigue can be accounted for.

4. **Standardisation**: Normalising EMG data provides a standardised measure of muscle activation, which can be useful in both research and clinical settings. For

Explain the purpose of normalising the EMG signal as %MVC

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