In　the　era　of　Internet　of　Things　(IoT),　human　activity　recognition　is　becoming　the　vital　underpinning　for　a　myriad　of　emerging　applications　in　smart　home　and　smart　buildings.　Existing　activity　recognition　approaches　require　either　the　deployment　of　extra　infrastructure　or　the　cooperation　of　occupants　to　carry　dedicated　devices,　which　are　expensive,　intrusive　and　inconvenient　for　pervasive　implementation.　In　this　paper,　we　propose　DeepSense,　a　device-free　human　activity　recognition　scheme　that　can　automatically　identify　common　activities　via　deep　learning　using　only　commodity　WiFi-enabled　IoT　devices.　We　design　a　novel　OpenWrt-based　IoT　platform　to　collect　Channel　State　Information　(CSI)　measurements　from　commercial　IoT　devices.　Moreover,　an　innovative　deep　learning　framework,　Autoencoder　Long-term　Recurrent　Convolutional　Network　(AE-LRCN),　is　proposed.　It　consists　of　an　autoencoder　module,　a　convolutional　neural　network　(CNN)　module　and　a　long　short-term　memory　(LSTM)　module,　which　aims　to　sanitize　the　noise　in　raw　CSI　data,　extract　high-level　representative　features　and　reveal　the　inherent　temporal　dependencies　among　data　for　accurate　human　activity　recognition,　respectively.　All　the　hyperparameters　in　AE-LRCN　are　fine-tuned　end-to-end　automatically.　Extensive　experiments　are　conducted　in　typical　indoor　environments　and　the　experimental　results　demonstrate　that　DeepSense　outperforms　existing　methods　and　achieves　a　97.4%　activity　recognition　accuracy　without　human　intervention.