Quasi-static human activities such as lying, standing or sitting produce very low Doppler shifts and highly spread radar signatures, making them difficult to detect with conventional constant–false–alarm rate (CFAR) detectors tuned for point targets. Moreover, privacy concerns and low lighting conditions limit the use of cameras in long–term care (LTC) facilities. This paper proposes a lightweight, non-visual image–based method for robust quasi-static human presence detection using a low–cost 60 GHz FMCW radar. On a dataset covering five semi-static activities, the proposed method improves average detection accuracy from 68.3% for Cell-Averaging CFAR (CA-CFAR) and 78.8% for Order-Statistics CFAR (OS-CFAR) to 85.4% for Subject 1, and from 51.3% and 68.3% to 79.5% for Subject 2. Finally, we benchmarked all three detectors across all activities on a Raspberry Pi 4B using a shared Range-Angle (RA) preprocessing pipeline. The proposed algorithm obtains an average 8.2 ms per frame, resulting in over 120 frames per second (FPS) and a 74 $\times$ speed-up over OS–CFAR. These results demonstrate that simple image–based processing can provide robust and deployable quasi-static human sensing in cluttered indoor environments.
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