Domain-Map Selection and Decision-Level Fusion for Lightweight FMCW Multi-Radar Human Activity Recognition

Abstract

Indoor human activity recognition using radar is a promising approach for privacy-preserving monitoring in assisted living and safety applications. However, radar-based recognition remains sensitive to viewpoint geometry because the same activity can produce different motion patterns when observed from different sensor positions. This work investigates whether compact per-radar classifiers and simple decision-level fusion are sufficient for reliable indoor human activity recognition using three frequency-modulated continuous-wave radars installed at different elevations. Point cloud detection files exported from the radar are converted into Doppler–Range, Doppler–Time, and Range–Time domain maps. A lightweight separable convolutional neural network is trained independently for each radar viewpoint, and session-level probability vectors are combined using mean fusion, entropy-weighted fusion, confidence-weighted fusion, and Dempster–Shafer fusion. Fusion is evaluated with session-level five-fold cross-validation, so every fused prediction comes from models that never saw that session during training. The per-radar probabilities are also temperature calibrated to check that the comparison between fusion rules is fair. The results show that viewpoint geometry strongly affects single-radar performance. Under leave-one-subject-out (LOSO) evaluation, the two frontal radars achieve macro F1 scores of 0.771 and 0.728, while the downward-angled radar reaches 0.480. Domain-map ablation shows that the Range–Time channel is the least informative map on every viewpoint. Differences among the stronger Doppler-based combinations stay within run-to-run training variation. Decision-level fusion gives the strongest session-level performance. The frontal pair reaches a macro F1 score of 0.952 with simple mean fusion and 0.964 with Dempster–Shafer fusion across 173 matched sessions. McNemar tests find no significant difference between mean fusion and the more complex rules. Fusing a strong radar with a much weaker viewpoint can even perform worse than the stronger radar alone.