Biosensing technology based on biomechanics in psycho analysis: Improving the efficiency of ideological and political education
Abstract
In recent years, advancements in technology have significantly transformed educational paradigms, particularly through the integration of biomechanics in teaching methodologies. The incorporation of biomechanical analysis in educational settings provides valuable insights into students' physical engagement and motor skills development. This study aims to leverage biomechanical data to enhance the effectiveness of physical education and sports training. Biomechanical sensors, such as motion capture systems and wearable devices, collect critical data on parameters like gait, balance, and muscle activity. By analyzing this data, educators can gain a deeper understanding of students' physical performance and identify areas for improvement. We propose a novel biomechanical optimization framework utilizing a multi-kernel support vector machine (MK-SVM) to assess students' physical strain levels during activities. In the preprocessing stage, a median filter is employed to eliminate noise from the motion data. Features are extracted using power spectral density (PSD) analysis to evaluate students' physical responses during instructional activities. The proposed method utilizes algorithms to create personalized training environments, identifying physical responses and facilitating real-time feedback for enhanced engagement in sports and physical education. The MK-SVM algorithm is applied for feature selection, effectively categorizing student strain levels to refine personalized learning strategies. Results indicate that our approach outperforms traditional methods, achieving high accuracy (92%), Recall (98%), precision (80%), and F1-Score (88%) in assessing students' physical strain. This study demonstrates how biomechanics and technology can revolutionize physical education, fostering more adaptive and responsive learning environments.
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