There　is　a　rapidly　increasing　demand　for　Human　Activity　Recognition　(HAR)　due　to　its　extensive　applications　in　various　fields　such　as　smart　homes,　healthcare,　nursing,　and　sports.　A　more　stable　and　powerful　system　that　can　adapt　to　various　complex　actual　environments　with　affordable　cost　of　data　acquisition　is　needed.　In　this　paper,　we　propose　a　domain　adaptive　human　activity　recognition　network　based　on　multimodal　feature　fusion　(DAMUN)　to　capture　information　of　data　from　FMCW　radar　and　USB　cameras.　In　the　network,　we　add　a　domain　discriminator　to　reduce　data　differences　due　to　the　changes　in　environments　and　user　habits.　In　order　to　reduce　the　workload　of　radar　data　acquisition　and　processing,　we　also　design　a　data　augmentation　model　based　on　a　generative　adversarial　network,　which　can　generate　radar　data　directly　from　image　data.　Finally,　we　implement　the　real-time　application　based　on　the　DAMUN　on　edge　computing　platforms.　The　experimental　results　show　that　the　proposed　network　achieves　obvious　advantages　over　the　existing　methods　and　can　effectively　adapt　to　different　environments.　In　addition,　the　network　can　meet　the　real-time　requirement　in　the　prediction　stage,　and　its　average　running　time　is　about　0.17s.　IEEE