Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity

Area of Science:

• Biomechanics
• Computational modeling
• Fuzzy logic

Background:

• Assessing lower limb peak torque is crucial for understanding physical activity and potential pathologies.
• Existing methods may not fully capture the nuances of physiological and pathological states.
• Fuzzy logic offers a novel approach to modeling complex physiological data.

Purpose of the Study:

• To construct a fuzzy computational model for evaluating lower limb peak torque in an isokinetic mode.
• To utilize hierarchical fuzzy logic for extracting computational rules from explicit data.
• To identify underlying physiological and pathological mechanisms related to lower limb function.

Main Methods:

• Involved 93 male participants: deaf soccer players, hearing soccer players, and untrained deaf males.
• Developed a fuzzy computational model focusing on lower limb physical activity.
• Employed hierarchical fuzzy logic to derive rules and mechanisms from data.

Main Results:

• The fuzzy model effectively assessed lower limb peak torque in an isokinetic mode.
• Demonstrated the model's capability in evaluating different physical activity levels.
• Successfully extracted computational rules to determine physiological and pathological mechanisms.

Conclusions:

• The proposed fuzzy model is effective for assessing lower limb peak torque.
• Hierarchical fuzzy logic provides a novel method for rule extraction and mechanism identification.
• Fuzzy parameters, including a dynamic norm, enhance the description of physiology, pathology, and rehabilitation.