Muscle Network Modeling Based on sEMG: A Comprehensive Review

Surface electromyography (sEMG) is a non-invasive technique that measures the electrical activity of muscles through electrodes placed on the skin. The use of sEMG has gained popularity in recent years as a tool for studying the neuromuscular system and its interactions with the environment. One of the most promising applications of sEMG is the development of muscle networks, which are mathematical models that simulate the interactions between muscles during movement. This review provides an overview of the current state of the art in muscle network modeling based on sEMG signals.

The first step in developing a muscle network is to collect sEMG data from multiple muscles during a movement task. The sEMG signals are then processed to extract features such as amplitude, frequency, and timing. These features are used as inputs to the muscle network model, which simulates the electrical activity of the muscles and their interactions with each other.

Several muscle network models have been proposed in the literature, each with its own strengths and limitations. One of the most widely used models is the Hill-type muscle model, which simulates the mechanical properties of muscles based on the length-tension relationship and force-velocity relationship. The Hill-type model has been used to study muscle coordination during tasks such as walking, running, and cycling.

Another popular muscle network model is the neural network model, which simulates the activity of motor neurons that control the muscles. The neural network model has been used to study the effects of fatigue on muscle coordination and to develop algorithms for controlling prosthetic limbs.

In addition to these models, other approaches have been proposed for developing muscle networks, such as fuzzy logic and artificial neural networks. These models have been used to study the effects of muscle activation patterns on joint forces and to develop control strategies for robots and exoskeletons.

Despite the progress made in muscle network modeling based on sEMG, there are still several challenges that need to be addressed. One of the main challenges is the variability of sEMG signals across individuals and tasks. This variability can affect the accuracy and reliability of the muscle network models. Another challenge is the complexity of the neuromuscular system, which involves multiple levels of organization, from the molecular level to the whole-body level.

In conclusion, muscle network modeling based on sEMG signals has the potential to provide valuable insights into the neuromuscular system and its interactions with the environment. However, further research is needed to overcome the challenges associated with variability and complexity. With continued development, muscle network modeling could lead to new advances in the fields of rehabilitation, prosthetics, and robotics.