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　8　×　8　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.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.