Research on machine learning-based anomaly detection techniques in biomechanical big data environments
Abstract
Anomaly detection is critical in identifying abnormal patterns in big data environments, where traditional techniques often struggle with scalability and efficiency. This paper explores machine learning-based anomaly detection techniques, focusing on their effectiveness in large-scale biomechanical data contexts. The study investigates three prominent methods: K-means clustering, autoencoders, and One-Class Support Vector Machine (SVM), each known for distinct strengths in handling biomechanical data. Through comprehensive simulations and experiments, precision, recall, F1-score, Area Under Curve (AUC), and time efficiency metrics are analyzed. The results highlight the trade-offs between accuracy and computational efficiency, offering insights into model performance in various biomechanical big data scenarios. The discussion emphasizes the suitability of autoencoders for detecting anomalies in complex biomechanical signals (e.g., gait analysis or joint kinematics) and the application of One-Class SVM in high-dimensional biomechanical datasets (e.g., muscle activation patterns or force plate data). The study concludes with recommendations for future research directions, including the integration of domain-specific biomechanical knowledge into machine learning models and the development of hybrid approaches for improved anomaly detection in biomechanics.
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