Vision-Inspired Image Quality Assessment for Radar-Based Human Activity Representations

TL;DR

This paper evaluates denoising techniques for radar-based human activity recognition, introduces a new static activity recognition framework, and proposes quality assessment algorithms to improve system reliability.

Contribution

It reimplements recent denoising methods, evaluates metrics in low-SNR conditions, and introduces a novel static activity recognition framework with quality scoring.

Findings

Enhanced classification accuracy for dynamic activities.

Improved interpretability of radar-based HAR systems.

Effective quality assessment algorithms for radar image fidelity.

Abstract

Radar-based human activity recognition has gained attention as a privacy-preserving alternative to vision and wearable sensors, especially in sensitive environments like long-term care facilities. Micro-Doppler spectrograms derived from FMCW radar signals are central to recognizing dynamic activities, but their effectiveness is limited by noise and clutter. In this work, we use a benchmark radar dataset to reimplement and assess three recent denoising and preprocessing techniques: adaptive preprocessing, adaptive thresholding, and entropy-based denoising. To illustrate the shortcomings of conventional metrics in low-SNR regimes, we evaluate performance using both perceptual image quality measures and standard error-based metrics. We additionally propose a novel framework for static activity recognition using range-angle feature maps to expand HAR beyond dynamic activities. We present two important contributions: a temporal tracking algorithm to enforce consistency and a no-reference quality scoring algorithm to assess RA-map fidelity. According to experimental findings, our suggested techniques enhance classification performance and interpretability for both dynamic and static activities, opening the door for more reliable radar-based HAR systems.