Submaximal normalizing methods to evaluate load sharing changes in the shoulder during repetitive work.

Area of Science:

• Biomechanics
• Human Movement Science
• Occupational Health

Background:

• Muscle fatigue alters the electromyography (EMG)-force relationship, complicating the assessment of muscle load sharing.
• Fatigue artifacts, such as increased EMG amplitude without force changes, can lead to misinterpretations of muscle activation patterns.

Purpose of the Study:

• To evaluate the effectiveness of normalizing EMG signals to repeated, static, submaximal exertions for mitigating fatigue artifacts.
• To compare novel EMG normalization methods against a standard method in the context of fatiguing work.

Main Methods:

• Participants performed repetitive work tasks to exhaustion while surface EMG was recorded from 11 shoulder muscles.
• Electromyography data were normalized using six methods: one standard and five novel techniques (Fatigue Only, Linear, Cubic, Points Forward, Points Forward/Backward).
• Novel methods were compared to the standard method using mixed-effects modeling to assess differences in normalized EMG amplitude.

Main Results:

• Significant differences in normalized EMG data were observed, varying by muscle and the number of time points analyzed (p < .05).
• The cubic normalization model demonstrated a better correlation with actual data points compared to linear predictions.
• The novel cubic normalization method effectively reduced fatigue effects, providing a more accurate representation of muscular loads.

Conclusions:

• The novel cubic normalization method shows promise in mitigating EMG fatigue artifacts.
• This method enhances the accuracy of muscle activity ratio calculations, better reflecting true muscular loads during fatiguing tasks.
• Accurate EMG normalization is crucial for understanding muscle function and load distribution in occupational and biomechanical studies.