This　article　presents　a　novel　millimeter-wave　(mm-wave)　radar-based　continuous　motion　detection　approach　called　mmCMD　for　the　long-term　care　of　the　elderly.　The　proposed　mmCMD　visualizes　the　micro-Doppler　signatures　of　continuous　motions　generated　from　millimeter　radar　as　images,　which　are　then　analyzed　by　an　object　detection　network　for　continuous　motion　detection.　To　improve　the　imaging　quality　of　micro-Doppler　signatures,　a　dynamic　feature　visualization　(DFV)　method　is　proposed　by　selectively　mapping　the　micro-Doppler　matrix　(MDM)　elements　with　significant　values,　highlighting　human　motion　to　enhance　subsequent　detection　network＇s　accuracy　in　capturing　the　details　of　the　motion.　Furthermore,　a　novel　detection　network　is　designed　for　the　visualized　micro-Doppler　images　by　combining　the　specially　designed　fusion　squeeze-and-excitation　(FSE)　module　with　the　coordinate　attention　(CA)　into　the　YOLOv5　architecture,　which　is　distinct　from　prior　works　that　overlook　global　contextual　information.　Experimental　results　demonstrate　that　the　proposed　mmCMD　achieves　a　mean　average　precision　(mAP)　of　93%　at　the　intersection　over　union　(IoU)　thresholds　from　0.5　to　0.95　and　an　F1　score　of　99%　for　12　actions,　which　makes　it　a　promising　solution　for　remotely　monitoring　and　detecting　elderly　individuals＇　activities　to　enhance　safety　and　risk　prevention　capabilities.