  This study explored an indoor system for tracking multiple humans and
detecting falls, employing three Millimeter-Wave radars from Texas Instruments.
Compared to wearables and camera methods, Millimeter-Wave radar is not plagued
by mobility inconveniences, lighting conditions, or privacy issues. We
conducted an initial evaluation of radar characteristics, covering aspects such
as interference between radars and coverage area. Then, we established a
real-time framework to integrate signals received from these radars, allowing
us to track the position and body status of human targets non-intrusively.
Additionally, we introduced innovative strategies, including dynamic
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering
based on signal SNR levels, a probability matrix for enhanced target tracking,
target status prediction for fall detection, and a feedback loop for noise
reduction. We conducted an extensive evaluation using over 300 min of data,
which equated to approximately 360,000 frames. Our prototype system exhibited a
remarkable performance, achieving a precision of 98.9% for tracking a single
target and 96.5% and 94.0% for tracking two and three targets in human-tracking
scenarios, respectively. Moreover, in the field of human fall detection, the
system demonstrates a high accuracy rate of 96.3%, underscoring its
effectiveness in distinguishing falls from other statuses.
