As athletes and sport professionals strive for peal performance, the ability to monitor and adjust training load, efforts and intensity becomes crucial. Training methods rely on indirect measurements like heart rate, GPS, location or video based analysis. While useful, these metrics are not capturing the full picture of muscular effort and performance. This is where electromyography (EMG) steps in, providing more precise insights into muscle activity. In this article, we explore how EMG offers more accurate training load and intensity assessments compared to other methods.
Understanding EMG and Its Functionality
Electromyography (EMG) is a technique used to measure the electrical activity produced by skeletal muscles during contraction and relaxation. When muscles are engaged, nerve impulses activate muscle fibers, generating electrical signals. Myontec has developed smart clothing technology with textile embedded EMG sensors to capture these signals accurately in real time, offering a direct window into muscle function ad further analysis.
Myontec Mbody shorts are measuring Quadriceps, Hamstring and Gluteus muscles while MSleeve is capturing Tibialis, Gastrocnemius and Soleus muscles. Detachable MCell is collecting, sampling and sending the captured EMG data but measuring motions (IMU), too.
Why EMG Is More Accurate Than Traditional Metrics?
Traditional training load assessments are often based on external factors such as GPS, IMU, location based sensors or video analysis. Heart rate as internal metrics is measuring cardio-vascular but not direct muscular load. These methods are indirect or imprecise because they don't account for specific muscle groups, load in small movements or static positioning - nor intensity levels across different exercises.
Pic 1. Game data in HR and EMG.
EMG, on the other hand, measures the actual electrical activity of the muscles in use, making it far more reliable for understanding muscle-specific workload and load in any position or movement. Here’s how EMG shines in comparison:
Direct Muscle Activity Measurement: EMG captures real-time data from the muscle, measuring the actual effort required for a given movement. This makes it especially useful for determining how different exercises target different muscles and for assessing how hard the muscles are working compared to heart rate or overall physical output measured with external sensors.
Pic 2. Same game as above but showing EMG distribution in big leg muscles.
Muscle-Specific Load Monitoring: With EMG, you can monitor specific muscle groups, helping athletes focus on weak points or overactive muscles. For example, during acceleration or jumps, EMG can highlight whether the quadriceps or glutes are carrying more load, enabling better training adjustments.
More Precise Intensity Levels: Heart rate or perceived exertion gives a generalized view of workout intensity, but EMG pinpoints the exact intensity of muscle engagement. For example, the same heart rate during running versus cycling doesn’t necessarily mean the same muscular load. EMG allows trainers to customize workouts for muscle-specific intensity.
Training Load Monitoring: EMG quantifies the cumulative electrical activity during exercise, giving a more accurate measurement of internal training load than external metrics like pace or heart rate. This is essential for avoiding overtraining or undertraining, allowing for tailored load adjustments.
See the video below how basketball club Pallacanestro Varese is monitoring internal load, effort and intensity with Myontec smart clothing solution during the game and training.
Conclusion
EMG provides a clear advantage when it comes to measuring training load and intensity with greater accuracy than traditional methods. By offering direct insights into muscle activation, EMG allows athletes to train smarter, avoid injuries, and optimize performance. Myontec smart clothing combines already EMG with IMU data giving better understanding of movement specific load and biomechanics. As wearable EMG technology continues to evolve, combining it with AI will open new doors for data-driven, personalized training programs, making it an indispensable tool in modern sports science.
References:
Porta, M., Filetti, C., Chiari, A. et al (2024)., Examining the association between speed and myoelectric activity: Time-based differences and muscle group balance, PLoS One. March 2024 https://pmc.ncbi.nlm.nih.gov/articles/PMC10936850/
Grassadonia, G. et al. (2024), Comparison of Metabolic Power and Energy Cost of Submaximal and Sprint Running Efforts Using Different Methods in Elite Youth Soccer Players: A Novel Energetic Approach, Sensors April 2024 https://www.mdpi.com/1424-8220/24/8/2577
Criswell, E. (2010), Cram’s Introduction to Surface Electromyography. 2nd ed. Lippincott Williams & Wilkins, 2010.