Sensor-based　human　activity　recognition　(HAR)　has　attracted　enormous　interests　due　to　its　wide　applications　in　the　Internet　of　Things　(IoT),　smart　homes　and　healthcare.　In　this　paper,　a　low-resolution　infrared　array　sensor-based　HAR　approach　is　proposed　using　the　deep　learning　framework.　The　device-free　sensing　system　leverages　the　infrared　array　sensor　of　8x8　pixels　to　collect　the　infrared　signals,　which　can　ensure　users＇　privacy　and　effectively　reduce　the　deployment　cost　of　the　network.　To　reduce　the　influence　of　temperature　variations,　a　combination　of　the　J-filter　noise　reduction　method　and　the　Butterworth　filter　is　performed　to　preprocess　the　infrared　signals.　Long　short-term　memory　(LSTM),　a　representative　recurrent　neural　network,　is　utilized　to　automatically　extract　characteristics　from　the　infrared　signal　and　build　the　recognition　model.　In　addition,　the　real-time　HAR　interface　is　designed　by　embedding　the　LSTM　model.　Experimental　results　show　that　the　typical　daily　activities　can　be　classified　with　the　recognition　accuracy　of　98.287%.　The　proposed　approach　yields　a　better　result　compared　to　the　existing　machine　learning　methods,　and　it　provides　a　low-cost　yet　promising　solution　for　privacy-preserving　scenarios.