Head Impact Detection Using Machine Learning Algorithms

– The need of precisely identifying head impacts and putting in place efficient safety measures has been underlined by numerous research investigations.. This study addresses this critical need by leveraging Logistic Regression, a simpler yet highly effective data collected from piezoelectric sensors installed on a model of a simulated cranium using a machine learning technique. We process normalized sensor data using Logistic Regression in a methodical manner with the goal of precisely identifying impact sites. The model achieves an impressive accuracy of 90%, demonstrating its capability to perform well in real-time applications that require quick, interpretable, and computationally efficient predictions. Rigorous evaluation the model’s performance is highlighted by employing k-fold cross-validation, while feature importance analysis identifies an optimal sensor placement strategy. This strategy may reduce model complexity, potentially leading to more efficient implementations without compromising predictive accuracy. The strong performance of Logistic Regression, coupled with its simplicity and interpretability, underscores its potential as an ideal solution for head impact detection in safety-critical environments .The results aid in the creation of intelligent safety systems, which combine machine learning and wearable technologies to boost safety and decision-making in both industrial and sporting settings.