A　fall　detection　module　is　an　important　component　of　community-based　care　for　the　elderly　to　reduce　their　health　risk.　It　requires　the　accuracy　of　detections　as　well　as　maintains　energy　saving.　In　order　to　meet　the　above　requirements,　a　sensing　module-integrated　energy-efficient　sensor　was　developed　which　can　sense　and　cache　the　data　of　human　activity　in　sleep　mode,　and　an　interrupt-driven　algorithm　is　proposed　to　transmit　the　data　to　a　server　integrated　with　ZigBee.　Secondly,　a　deep　neural　network　for　fall　detection　(FD-DNN)　running　on　the　server　is　carefully　designed　to　detect　falls　accurately.　FD-DNN,　which　combines　the　convolutional　neural　networks　(CNN)　with　long　short-term　memory　(LSTM)　algorithms,　was　tested　on　both　with　online　and　offline　datasets.　The　experimental　result　shows　that　it　takes　advantage　of　CNN　and　LSTM,　and　achieved　99.17%　fall　detection　accuracy,　while　its　specificity　and　sensitivity　are　99.94%　and　94.09%,　respectively.　Meanwhile,　it　has　the　characteristics　of　low　power　consumption.