Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition

Abstract

Background: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly care. However, traditional HAR methods relying on accelerometer data often require complex preprocessing steps, including noise reduction and manual feature extraction. Deep learning-based human activity recognition (HAR) using one-dimensional accelerometer data often suffers from noise and limited feature extraction. Transforming time-series signals into two-dimensional representations has shown potential for enhancing feature extraction and reducing noise. However, existing methods relying on single-feature inputs or extensive preprocessing face limitations in robustness and accuracy. Methods: This study proposes a multi-input, two-dimensional CNN architecture using three distinct data reconstruction methods. By fusing features from reconstructed images, the model enhances feature extraction capabilities. This method was validated on a custom HAR dataset without requiring complex preprocessing steps. Results: The proposed method outperformed models using single-reconstruction methods or raw one-dimensional data. Compared to a one-dimensional baseline, it achieved 16.64%, 13.53%, and 16.3% improvements in accuracy, precision, and recall, respectively. We tested across various levels of noise, and the proposed model consistently demonstrated greater robustness than the time-series-based approach. Fusing features from three inputs effectively captured latent patterns and variations in accelerometer data. Conclusions: This study demonstrates that HAR can be effectively improved using a multi-input CNN approach with reconstructed data. This method offers a practical and efficient solution, streamlining feature extraction and enhancing performance, making it suitable for real-world applications.