Abstract
Plantar pressure measurement is an integral aspect of biomedical engineering, providing important insights for diagnosing and managing foot-related health conditions. This study introduces a modular piezoelectric sensing insole designed for enhanced human gait analysis. The system incorporates force-sensitive resistors strategically placed to capture dynamic plantar pressure data, which is processed and visualized using Python-based tools. Emphasizing portability, cost-effectiveness, and real-time analysis, the system identifies abnormal pressure distributions and classifies foot conditions. By integrating machine learning algorithm XGBoost, the solution provides actionable insights, aiding healthcare professionals in early diagnosis and preventive interventions. This approach offers substantial benefits in clinical settings, sports science, and rehabilitation, bridging the gap between technology and personalized healthcare.
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