The　millimeter-wave　(mmWave)　radar　technology　has　attracted　significant　attention　because　it　is　susceptible　to　environmental　lighting,　wall　shielding,　and　privacy　concern.　This　article　proposes　a　novel　noninvasive　human　activity　recognition　system　using　a　mmWave　radar.　The　proposed　framework　first　transforms　mmWave　signals　into　point　clouds.　Generally　speaking,　it　consists　of　four　major　components:　denosing,　enhanced　voxelization,　data　augmentation,　and　dual-view　machine　learning　to　lead　to　accurate　and　efficient　human　activity　recognition.　The　proposed　new　methodology　considers　the　spatial-temporal　point　clouds　in　physical　environments　through　a　modified　voxelization　approach,　enriches　the　sparse　data　based　on　the　symmetry　property　of　radar　rotations,　and　learns　the　activity　using　a　dual-view　convolutional　neural　network.　To　evaluate　the　performance　of　the　proposed　learning　models,　a　dataset　involving　seven　different　activities　has　been　established　using　a　mmWave　radar　platform.　The　experimental　results　have　demonstrated　that　the　proposed　system　can　achieve　97.61%　and　98%　accuracies　during　the　tests　of　fall　detection　and　activity　classification,　respectively.　In　comparison,　the　proposed　scheme　greatly　outperforms　four　other　conventional　machine　learning　schemes　in　terms　of　the　overall　accuracy.