Radar-Based Short-Time Frequency Accumulation for Masked Fall Detection

Masked situations, prevalent in complex scenarios involving multiple people or objects, can be defined as cases where the line of sight between radar and a target is partially obstructed. These situations, with degraded information received by radar, bring challenges for fall detection using conventional approaches. This article introduces a novel approach to detecting falls in masked scenarios using a millimeter-wave (mmWave) radar. The proposed approach combines two main features extracted from the radar signals: 1) the pattern-contour-confined DC-corrected Doppler time (PCC-DCDT) map, generated from DC-corrected radar signals, and 2) the short-time frequency accumulation (STFA), proposed for enhancement of the weakened information in masked situations. Experimental results demonstrate that the combination of the PCC-DCDT map and STFA improves the precision and reliability in masked situations. This exploration work on masked fall scenarios adapts radar sensors to realistic indoor environments with improved accuracy, providing sufficient detection robustness for ubiquitous healthcare applications.