Sedentary　behavior　(SB)　has　been　proved　to　be　an　important　risk　factor　for　poor　health,　such　as　blood　pressure　and　even　cancer.　However,　existing　sensor-and　vision-based　SB　detection　approaches　have　limitations　on　practical　usage　and　privacy　concerns,　respectively.　In　this　paper,　we　take　the　first　attempt　to　develop　a　device-free　SB　monitoring　and　recommendation　system　namely　CareFi,　which　leverages　tremendous　information　behind　WiFi　signals　tomonitor　the　indoor　environment　and　identify　series　of　activities　in　SB.　We　deeply　investigate　the　properties　of　channel　state　information　and　various　activities　in　SB.　According　to　different　characteristics　of　static　and　dynamic　activities,　we　design　a　foreground　detection　method　to　separate　two　categories　and　then　adopt　discriminative　features　of　wireless　signals　in　the　frequency　and　time　domains　to　recognize　them.　Besides,　we　propose　an　updated　strategy　to　overcome　the　mutability　of　environment.　We　implement　CareFi　on　commercial　off-the-shelf　WiFi　routers　and　evaluate　its　performance　in　both　office　and　home　environments.　Experimental　results　demonstrate　the　robustness　and　accuracy　of　our　method.