Neural Signal Denoising in Sports Biomechanics: Dynamic Arrow Characterization with Stacking Ensemble of Hybrid CNN-RNN

Abstract

In the field of sports biomechanics, the analysis of dynamic motion patterns is crucial for understanding athletic performance, injury prevention, and rehabilitation strategies. However, capturing clean and accurate neural signals from athletes during high-intensity activities can be challenging due to various sources of noise and interference. This research proposes a novel approach to neural signal denoising using a stacking ensemble of hybrid Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for dynamic arrow characterization in sports biomechanics. The proposed method combines the strengths of both CNN and RNN architectures to capture spatial and temporal relationships within the noisy neural signals. The CNN component is responsible for extracting relevant features from the raw data, while the RNN component models the temporal dependencies within the denoised signals. By employing a stacking ensemble technique, the model leverages the outputs from multiple hybrid CNN-RNN models to enhance overall performance and robustness. The proposed approach is evaluated on a comprehensive dataset collected from athletes performing various sports-specific movements. The results demonstrate the effectiveness of the stacking ensemble method in accurately denoising neural signals and characterizing dynamic arrow patterns, outperforming traditional denoising techniques and single-model approaches. The findings of this research have significant implications for sports biomechanics, enabling researchers and practitioners to obtain more reliable and accurate neural data, leading to improved analysis, decision-making, and performance optimization. The proposed method can be extended to other domains where signal denoising and dynamic pattern characterization are critical, such as healthcare, robotics, and human-computer interaction.