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This study examines how electrical stimulation of the soleus muscle can help improve the control of blood glucose and blood lipids. Conditions such as diabetes and obesity are often linked to poor insulin action and disturbed fat metabolism, which creates a need for non-drug interventions. We build a mathematical model that combines glucose–insulin dynamics, oxygen consumption ( \(\text {VO}_2\) ), and fat and carbohydrate oxidation to describe the metabolic effects of soleus stimulation. We then use Particle Swarm Optimization (PSO), a bio-inspired search method, to find the best stimulation intensity and frequency that increase glucose use while also promoting fat oxidation. The model shows that high-intensity (100% MVC) and high-frequency (50 Hz) stimulation provide the most favorable metabolic effects. This work offers a simple computational framework to adjust electrical stimulation parameters in a data-driven way and supports future clinical use, including wearable EMS devices for personalized metabolic therapy.

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Optimizing Soleus Muscle Stimulation for Glucose and Lipid Regulation: A Mathematical Modeling and Particle Swarm Optimization Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Mohanad A. Deif,
  • Mohamed Elhoseny,
  • Mohamed A. Hafez

摘要

This study examines how electrical stimulation of the soleus muscle can help improve the control of blood glucose and blood lipids. Conditions such as diabetes and obesity are often linked to poor insulin action and disturbed fat metabolism, which creates a need for non-drug interventions. We build a mathematical model that combines glucose–insulin dynamics, oxygen consumption ( \(\text {VO}_2\) ), and fat and carbohydrate oxidation to describe the metabolic effects of soleus stimulation. We then use Particle Swarm Optimization (PSO), a bio-inspired search method, to find the best stimulation intensity and frequency that increase glucose use while also promoting fat oxidation. The model shows that high-intensity (100% MVC) and high-frequency (50 Hz) stimulation provide the most favorable metabolic effects. This work offers a simple computational framework to adjust electrical stimulation parameters in a data-driven way and supports future clinical use, including wearable EMS devices for personalized metabolic therapy.