This　paper　presents　the　research　and　application　of　real-Time　fall　detection　system　based　on　wearable　data　fusion.　Firstly,　it　builds　the　activity　model　of　elderly　people　based　on　attitude　angles,　designs　and　develops　the　sensor　board　integrated　with　three-Axis　accelerometer,　gyroscope　and　Bluetooth　to　collect　the　activity　data　of　the　elderly　in　real　time　and　send　them　to　the　smart　mobile　phone　through　Bluetooth.　Secondly,　it　chooses　attitude　angles　and　acceleration　signal　vector　magnitude　as　the　features　of　fall　detection,　and　accomplish　the　denoising　treatment　for　attitude　angles　by　Kalman　filter.　It　uses　the　sliding　window　and　k-NN　algorithm　to　extract　features　and　implements　the　system　that　can　detect　the　fall　of　the　elderly　and　give　an　alarm.　Finally,　the　simulated　experiment　results　provided　by　Weka　show　that　the　accuracy　rate　of　the　fall　detection　method　is　95.8%,　and　the　average　sensitivity　and　average　specificity　of　different　activities　are　up　to　95.8%　and　99.2%　respectively,　which　proves　that　the　method　has　the　feature　of　good　real-Time　performance　and　high　accuracy.　©　2017　IEEE.