Improving human activity classification based on micro-doppler signatures of FMCW radar with the effect of noise.

ABSTRACT: Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from

SUBMITTER: Nguyen N 

PROVIDER: S-EPMC11293691 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature