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

Abstract

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